From 81a6d8eb2162bc0190010bf9b692394b969f3b3e Mon Sep 17 00:00:00 2001
From: Zach Deane-Mayer <581590+zachmayer@users.noreply.github.com>
Date: Fri, 9 Aug 2024 16:32:44 -0400
Subject: [PATCH] Test reorg (#308)

* put 2 files of random tests into the correct test files

* reorg-mopre

* miss one coverage line

* 100% test coverage

* some more reorg

* remove redudant tests, but also removed too many stop cases

* refactor caretList tests

* fix lint

* re add cases to get to 100%

* utils::

* refactor caretEnsemble tests

* refactor multiclass tests

* refactor class selection tests

* done with test refactor

* tests fail on some machines, loosen them up
---
 Makefile                                    |  30 +-
 coverage.rds                                | Bin 175137 -> 175079 bytes
 tests/testthat/test-S3-generic-extensions.R | 203 -----
 tests/testthat/test-caretEnsemble.R         | 365 +++++----
 tests/testthat/test-caretList.R             | 800 ++++----------------
 tests/testthat/test-caretPredict.R          | 197 ++++-
 tests/testthat/test-caretStack.R            | 465 ++++++------
 tests/testthat/test-classSelection.R        |  68 +-
 tests/testthat/test-ensembleMethods.R       | 115 ---
 tests/testthat/test-helper_functions.R      | 444 -----------
 tests/testthat/test-multiclass.R            | 175 ++---
 tests/testthat/test-permutationImportance.R | 281 +++----
 12 files changed, 965 insertions(+), 2178 deletions(-)
 delete mode 100644 tests/testthat/test-S3-generic-extensions.R
 delete mode 100644 tests/testthat/test-ensembleMethods.R
 delete mode 100644 tests/testthat/test-helper_functions.R

diff --git a/Makefile b/Makefile
index 6513f3ea..52d08fb2 100644
--- a/Makefile
+++ b/Makefile
@@ -3,21 +3,21 @@
 .PHONY: help
 help:
 	@echo "Available targets:"
-	@echo "  all                   - Run clean, fix-style, document, install, build-vignettes, lint, spell, test, check, coverage"
-	@echo "  install-deps          - Install dependencies"
-	@echo "  install               - Install the whole package, including dependencies"
-	@echo "  document              - Generate documentation"
-	@echo "  update-test-fixtures  - Update test fixtures"
-	@echo "  test                  - Run unit tests"
-	@echo "  coverage              - Generate coverage reports"
-	@echo "  check                 - Run R CMD check as CRAN"
-	@echo "  fix-style             - Auto style the code"
-	@echo "  lint                  - Check the code for lint"
-	@echo "  spell                 - Check spelling"
-	@echo "  build                 - Build the package"
-	@echo "  build-vignettes       - Build vignettes"
-	@echo "  release               - Release to CRAN"
-	@echo "  clean                 - Clean up generated files"
+	@echo "  all                    Run clean, fix-style, document, install, build-vignettes, lint, spell, test, check, coverage"
+	@echo "  install-deps           Install dependencies"
+	@echo "  install                Install the whole package, including dependencies"
+	@echo "  document               Generate documentation"
+	@echo "  update-test-fixtures   Update test fixtures"
+	@echo "  test                   Run unit tests"
+	@echo "  coverage               Generate coverage reports"
+	@echo "  check                  Run R CMD check as CRAN"
+	@echo "  fix-style              Auto style the code"
+	@echo "  lint                   Check the code for lint"
+	@echo "  spell                  Check spelling"
+	@echo "  build                  Build the package"
+	@echo "  build-vignettes        Build vignettes"
+	@echo "  release                Release to CRAN"
+	@echo "  clean                  Clean up generated files"
 
 .PHONY: all
 all: clean fix-style document install build-vignettes lint spell test check coverage
diff --git a/coverage.rds b/coverage.rds
index e427756365e82eaac41071085f0880033d11a324..247b11a103a8b995a6bf02d968537fb35025d478 100644
GIT binary patch
delta 103307
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diff --git a/tests/testthat/test-S3-generic-extensions.R b/tests/testthat/test-S3-generic-extensions.R
deleted file mode 100644
index 1a6f3aee..00000000
--- a/tests/testthat/test-S3-generic-extensions.R
+++ /dev/null
@@ -1,203 +0,0 @@
-# test-S3-generic-extensions
-set.seed(107L)
-
-data(models.class)
-data(X.class)
-data(Y.class)
-data(models.reg)
-data(X.reg)
-data(Y.reg)
-
-data(Sonar, package = "mlbench")
-
-# A common control to use for both test fixtures
-my_control <- caret::trainControl(
-  method = "cv",
-  number = 2L,
-  savePredictions = "final",
-  summaryFunction = caret::twoClassSummary,
-  classProbs = TRUE,
-  verboseIter = FALSE,
-  index = caret::createResample(Sonar$Class, 3L)
-)
-
-# a model of class caretList
-model_list1 <- caretList(
-  Class ~ .,
-  data = Sonar,
-  trControl = my_control,
-  tuneList = list(
-    glm = caretModelSpec(method = "rf"),
-    rpart = caretModelSpec(method = "rpart")
-  )
-)
-
-# a model of class train
-rpart_model <- caret::train(
-  Class ~ .,
-  data = Sonar,
-  tuneLength = 2L,
-  metric = "ROC",
-  trControl = my_control,
-  method = "rpart"
-)
-
-###############################################
-testthat::context("Ancillary caretList S3 Generic Functions Extensions")
-################################################
-testthat::test_that("c.caretEnsemble can bind two caretList objects", {
-  model_list2 <- caretList(
-    Class ~ .,
-    data = Sonar,
-    trControl = my_control,
-    tuneList = list(
-      glm = caretModelSpec(method = "rpart", tuneLength = 2L),
-      rpart = caretModelSpec(method = "rf", tuneLength = 2L)
-    )
-  )
-
-  bigList <- c(model_list1, model_list2)
-  ens1 <- caretEnsemble(bigList)
-
-  testthat::expect_is(bigList, "caretList")
-  testthat::expect_is(ens1, "caretEnsemble")
-  testthat::expect_identical(anyDuplicated(names(bigList)), 0L)
-  testthat::expect_length(unique(names(bigList)), 4L)
-})
-
-testthat::test_that("c.caretEnsemble can bind a caretList and train object", {
-  bigList <- c(model_list1, rpart_model)
-  ens1 <- caretEnsemble(bigList)
-
-  testthat::expect_is(bigList, "caretList")
-  testthat::expect_is(ens1, "caretEnsemble")
-  testthat::expect_identical(anyDuplicated(names(bigList)), 0L)
-  testthat::expect_length(unique(names(bigList)), 3L)
-})
-
-testthat::test_that("c.caretList can bind two objects of class train", {
-  bigList <- c(rpart_model, rpart_model)
-  ens1 <- caretEnsemble(bigList)
-
-  testthat::expect_is(bigList, "caretList")
-  testthat::expect_is(ens1, "caretEnsemble")
-
-  testthat::expect_identical(anyDuplicated(names(bigList)), 0L)
-  testthat::expect_length(unique(names(bigList)), 2L)
-})
-
-testthat::test_that("c.caretList stops for invalid class", {
-  testthat::expect_error(c.caretList(list()), "class of modelList1 must be 'caretList' or 'train'")
-})
-
-testthat::test_that("c.train stops for invalid class", {
-  testthat::expect_error(c.train(list()), "class of modelList1 must be 'caretList' or 'train'")
-})
-
-###############################################
-testthat::context("Edge cases for caretList S3 Generic Functions Extensions")
-################################################
-
-testthat::test_that("c.caretList combines caretList objects correctly", {
-  # Split models.class into two parts
-  models_class1 <- models.class[1L:2L]
-  models_class2 <- models.class[3L:4L]
-  class(models_class1) <- class(models_class2) <- "caretList"
-
-  combined_models <- c(models_class1, models_class2)
-
-  testthat::expect_s3_class(combined_models, "caretList")
-  testthat::expect_length(combined_models, length(models.class))
-  testthat::expect_true(all(names(combined_models) %in% names(models.class)))
-})
-
-testthat::test_that("c.caretList combines caretList and train objects correctly", {
-  models_class1 <- models.class[1L:2L]
-  class(models_class1) <- "caretList"
-  single_model <- models.class[[3L]]
-
-  combined_models <- c(models_class1, single_model)
-
-  testthat::expect_s3_class(combined_models, "caretList")
-  testthat::expect_length(combined_models, 3L)
-  testthat::expect_true(all(names(combined_models) %in% names(models.class)))
-})
-
-testthat::test_that("c.train combines train objects correctly", {
-  model1 <- models.class[[1L]]
-  model2 <- models.class[[2L]]
-
-  combined_models <- c(model1, model2)
-
-  testthat::expect_s3_class(combined_models, "caretList")
-  testthat::expect_length(combined_models, 2L)
-  testthat::expect_true(all(names(combined_models) %in% names(models.class)[1L:2L]))
-})
-
-testthat::test_that("c.caretList handles duplicate names", {
-  models_class1 <- models.class[1L:2L]
-  class(models_class1) <- "caretList"
-
-  combined_models <- c(models_class1, models_class1)
-
-  testthat::expect_s3_class(combined_models, "caretList")
-  testthat::expect_length(combined_models, 4L)
-  testthat::expect_true(all(make.names(rep(names(models_class1), 2L), unique = TRUE) %in% names(combined_models)))
-})
-
-testthat::test_that("c.caretList and c.train fail for invalid inputs", {
-  testthat::expect_error(c.caretList(list(a = 1L, b = 2L)), "class of modelList1 must be 'caretList' or 'train'")
-  testthat::expect_error(c.train(list(a = 1L, b = 2L)), "class of modelList1 must be 'caretList' or 'train'")
-})
-
-testthat::test_that("[.caretList subsets caretList objects correctly", {
-  subset_models <- models.class[1L:2L]
-
-  testthat::expect_s3_class(subset_models, "caretList")
-  testthat::expect_length(subset_models, 2L)
-  testthat::expect_true(all(names(subset_models) %in% names(models.class)[1L:2L]))
-})
-
-testthat::test_that("as.caretList.list converts list to caretList", {
-  model_list <- list(model1 = models.class[[1L]], model2 = models.class[[2L]])
-  caretlist_object <- as.caretList(model_list)
-
-  testthat::expect_s3_class(caretlist_object, "caretList")
-  testthat::expect_length(caretlist_object, 2L)
-  testthat::expect_true(all(names(caretlist_object) %in% names(model_list)))
-})
-
-testthat::test_that("as.caretList.list fails for invalid inputs", {
-  testthat::expect_error(as.caretList(list(a = 1L, b = 2L)), "object requires all elements of list to be caret models")
-})
-
-testthat::test_that("as.caretList.list names lists without names", {
-  models.no.name <- models.class
-  names(models.no.name) <- NULL
-  class(models.no.name) <- "list"
-  testthat::expect_null(names(models.no.name))
-  cl <- as.caretList(models.no.name)
-  testthat::expect_named(cl, unname(vapply(models.class, "[[", character(1L), "method")))
-})
-testthat::test_that("as.caretList fails on non-list", {
-  testthat::expect_error(as.caretList(1L), "object must be a list")
-})
-
-testthat::test_that("predict.caretList works for classification and regression", {
-  class_preds <- predict(models.class, newdata = X.class, excluded_class_id = 0L)
-  reg_preds <- predict(models.reg, newdata = X.reg)
-
-  testthat::expect_is(class_preds, "data.table")
-  testthat::expect_is(reg_preds, "data.table")
-  testthat::expect_identical(nrow(class_preds), nrow(X.class))
-  testthat::expect_identical(nrow(reg_preds), nrow(X.reg))
-  testthat::expect_identical(ncol(class_preds), length(models.class) * 2L)
-  testthat::expect_identical(ncol(reg_preds), length(models.reg))
-})
-
-testthat::test_that("predict.caretList handles type='prob' for classification", {
-  class_probs <- predict(models.class, newdata = X.class, excluded_class_id = 0L)
-  testthat::expect_is(class_probs, "data.table")
-  testthat::expect_identical(nrow(class_probs), nrow(X.class))
-  testthat::expect_identical(ncol(class_probs), length(models.class) * nlevels(Y.class))
-})
diff --git a/tests/testthat/test-caretEnsemble.R b/tests/testthat/test-caretEnsemble.R
index 0390a260..4a661551 100644
--- a/tests/testthat/test-caretEnsemble.R
+++ b/tests/testthat/test-caretEnsemble.R
@@ -1,66 +1,105 @@
-# Are tests failing here?
-# UPDATE THE FIXTURES!
-# make update-test-fixtures
-
-data(models.reg)
-data(X.reg)
-data(Y.reg)
-
-data(models.class)
-data(X.class)
-data(Y.class)
-
+# Load required data
+utils::data(models.reg)
+utils::data(X.reg)
+utils::data(Y.reg)
+utils::data(models.class)
+utils::data(X.class)
+utils::data(Y.class)
+utils::data(Sonar, package = "mlbench")
+
+# Set up test environment
 set.seed(1234L)
+k <- 2L
 ens.reg <- caretEnsemble(
   models.reg,
-  trControl = caret::`trainControl`(method = "cv", number = 2L, savePredictions = "final")
+  trControl = caret::trainControl(
+    method = "cv",
+    number = k,
+    index = caret::createFolds(Y.reg, k = k),
+    savePredictions = "final"
+  )
+)
+
+ens.class <- caretEnsemble(
+  models.class,
+  metric = "ROC",
+  trControl = caret::trainControl(
+    method = "cv",
+    number = k,
+    index = caret::createFolds(Y.class, k = k),
+    summaryFunction = caret::twoClassSummary,
+    classProbs = TRUE,
+    savePredictions = TRUE
+  )
 )
 
-#############################################################################
-testthat::context("Test metric and residual extraction")
-#############################################################################
+# Helper function for prediction tests
+test_predictions <- function(ens, newdata, one_row_preds) {
+  is_class <- isClassifier(ens)
+  N <- nrow(newdata)
+
+  pred_stacked <- predict(ens)
+  pred <- predict(ens, newdata = newdata)
+  pred_se <- predict(ens.reg, newdata = X.reg, se = TRUE)
+  pred_one <- predict(ens, newdata = newdata[1L, , drop = FALSE])
+
+  testthat::expect_s3_class(pred_stacked, "data.table")
+  testthat::expect_s3_class(pred, "data.table")
+  testthat::expect_s3_class(pred_se, "data.table")
+  testthat::expect_s3_class(pred_one, "data.table")
+
+  testthat::expect_identical(nrow(pred_stacked), N)
+  testthat::expect_identical(nrow(pred), N)
+  testthat::expect_identical(nrow(pred_se), N)
+  testthat::expect_identical(nrow(pred_one), 1L)
+
+  testthat::expect_equal(pred_stacked, pred, tol = ifelse(is_class, 0.35, 0.05))
+
+  if (is_class) {
+    testthat::expect_identical(ncol(pred_stacked), 2L)
+    testthat::expect_identical(ncol(pred), 2L)
+    testthat::expect_identical(ncol(pred_one), 2L)
+    testthat::expect_equivalent(pred_one$Yes, one_row_preds[1L], tol = 0.05)
+    testthat::expect_equivalent(pred_one$No, one_row_preds[2L], tol = 0.05)
+  } else {
+    testthat::expect_equivalent(pred_one$pred, one_row_preds[1L], tol = 0.05)
+  }
+}
 
-testthat::test_that("We can extract resdiuals from train regression objects", {
+######################################################################
+testthat::context("Metric and residual extraction")
+######################################################################
+
+testthat::test_that("We can extract residuals from train regression objects", {
   data(iris)
-  mod <- caret::train(
-    iris[, 1L:2L], iris[, 3L],
-    method = "lm"
-  )
+  mod <- caret::train(iris[, 1L:2L], iris[, 3L], method = "lm")
   r <- stats::residuals(mod)
   testthat::expect_is(r, "numeric")
   testthat::expect_length(r, 150L)
 })
 
-#############################################################################
-testthat::context("Does ensembling and prediction work?")
-#############################################################################
+######################################################################
+testthat::context("Ensembling and prediction")
+######################################################################
 
 testthat::test_that("We can ensemble regression models", {
   testthat::expect_s3_class(ens.reg, "caretEnsemble")
-  pred.reg <- predict(ens.reg, newdata = X.reg)
-  pred.reg2 <- predict(ens.reg, newdata = X.reg, se = TRUE)
-
-  testthat::expect_true(all(pred.reg == pred.reg2$pred))
-
+  test_predictions(ens.reg, X.reg, 5.04)
+})
 
-  testthat::expect_s3_class(pred.reg, "data.table")
-  testthat::expect_identical(nrow(pred.reg), 150L)
-  ens.class <- caretEnsemble(models.class)
+testthat::test_that("We can ensemble classification models", {
   testthat::expect_s3_class(ens.class, "caretEnsemble")
-  pred.class <- predict(ens.class, newdata = X.class)
-  testthat::expect_s3_class(pred.class, "data.table")
-  testthat::expect_identical(nrow(pred.class), 150L)
+  test_predictions(ens.class, X.class, c(0.02, 0.98))
 })
 
-#############################################################################
-testthat::context("Does ensembling work with models with differing predictors")
-#############################################################################
+######################################################################
+testthat::context("Ensembling with models of differing predictors")
+######################################################################
 
 testthat::test_that("We can ensemble models of different predictors", {
   data(iris)
   Y.reg <- iris[, 1L]
   X.reg <- model.matrix(~., iris[, -1L])
-  mseeds <- vector(mode = "list", length = 12L)
   my_control <- caret::trainControl(
     method = "cv", number = 2L,
     p = 0.75,
@@ -77,122 +116,45 @@ testthat::test_that("We can ensemble models of different predictors", {
   )
   nestedList <- as.caretList(nestedList)
 
-  # Can we predict from the list
   pred_list <- predict(nestedList, newdata = X.reg)
   testthat::expect_s3_class(pred_list, "data.table")
   testthat::expect_identical(nrow(pred_list), 150L)
   testthat::expect_identical(ncol(pred_list), length(nestedList))
 
-  # Can we predict from the ensemble
   ensNest <- caretEnsemble(nestedList)
   testthat::expect_s3_class(ensNest, "caretEnsemble")
   pred.nest <- predict(ensNest, newdata = X.reg)
   testthat::expect_s3_class(pred.nest, "data.table")
   testthat::expect_identical(nrow(pred.nest), 150L)
 
-  # Ensemble errors on NAs
   X_reg_new <- X.reg
   X_reg_new[2L, 3L] <- NA
-  expect_error(
+  testthat::expect_error(
     predict(ensNest, newdata = X_reg_new),
     "is.finite(newdata) are not all TRUE",
     fixed = TRUE
   )
 })
 
-testthat::context("Does ensemble prediction work with new data")
-
-testthat::test_that("caretEnsemble works for regression models", {
-  set.seed(1234L)
-  testthat::expect_is(ens.reg, "caretEnsemble")
-
-  # Predictions
-  pred_stacked <- predict(ens.reg) # stacked predictions
-  pred_in_sample <- predict(ens.reg, newdata = X.reg) # in sample predictions
-  pred_one <- predict(ens.reg, newdata = X.reg[2L, , drop = FALSE]) # one row predictions
-
-  # Check class
-  testthat::expect_s3_class(pred_stacked, "data.table")
-  testthat::expect_s3_class(pred_in_sample, "data.table")
-  testthat::expect_s3_class(pred_one, "data.table")
-
-  # Check len
-  testthat::expect_identical(nrow(pred_stacked), 150L)
-  testthat::expect_identical(nrow(pred_in_sample), 150L)
-  testthat::expect_identical(nrow(pred_one), 1L)
-
-  # stacked predcitons should be similar to in sample predictions
-  testthat::expect_equal(pred_stacked, pred_in_sample, tol = 0.1)
-
-  # One row predictions
-  testthat::expect_equivalent(pred_one$pred, 4.712639, tol = 0.05)
-})
-
-testthat::test_that("caretEnsemble works for classification models", {
-  set.seed(1234L)
-  ens.class <- caretEnsemble(
-    models.class,
-    trControl = caret::trainControl(
-      method = "cv",
-      number = 10L,
-      savePredictions = "final",
-      classProbs = TRUE
-    )
-  )
-  testthat::expect_s3_class(ens.class, "caretEnsemble")
-  ens.class$ens_model$finalModel
-
-  # Predictions
-  pred_stacked <- predict(ens.class) # stacked predictions
-  pred_in_sample <- predict(ens.class, newdata = X.class) # in sample predictions
-  pred_one <- predict(ens.class, newdata = X.class[2L, , drop = FALSE]) # one row predictions
-
-  # Check class
-  testthat::expect_s3_class(pred_stacked, "data.table")
-  testthat::expect_s3_class(pred_in_sample, "data.table")
-  testthat::expect_s3_class(pred_one, "data.table")
-
-  # Check rows
-  testthat::expect_identical(nrow(pred_stacked), 150L)
-  testthat::expect_identical(nrow(pred_in_sample), 150L)
-  testthat::expect_identical(nrow(pred_one), 1L)
-
-  # Check cols
-  testthat::expect_identical(ncol(pred_stacked), 2L)
-  testthat::expect_identical(ncol(pred_in_sample), 2L)
-  testthat::expect_identical(ncol(pred_one), 2L)
-
-  # stacked predcitons should be similar to in sample predictions
-  testthat::expect_equal(pred_stacked, pred_in_sample, tol = 0.1)
-
-  # One row predictions
-  testthat::expect_equivalent(pred_one$Yes, 0.02, tol = 0.05)
-  testthat::expect_equivalent(pred_one$No, 0.98, tol = 0.05)
-})
-
-testthat::context("Do ensembles of custom models work?")
+######################################################################
+testthat::context("Ensembles with custom models")
+######################################################################
 
 testthat::test_that("Ensembles using custom models work correctly", {
   set.seed(1234L)
 
-  # Create custom caret models with a properly assigned method attribute
   custom.rf <- getModelInfo("rf", regex = FALSE)[[1L]]
   custom.rf$method <- "custom.rf"
 
   custom.rpart <- getModelInfo("rpart", regex = FALSE)[[1L]]
   custom.rpart$method <- "custom.rpart"
 
-  # Define models to be used in ensemble
-  # Add an unnamed model to ensure that method names are extracted from model info
-  # Add a named custom model, to contrast the above
-  # Add a non-custom model
   tune.list <- list(
     caretModelSpec(method = custom.rf, tuneLength = 1L),
     myrpart = caretModelSpec(method = custom.rpart, tuneLength = 1L),
     treebag = caretModelSpec(method = "treebag", tuneLength = 1L)
   )
 
-  # Create an ensemble using the above models
   cl <- caretList(X.class, Y.class, tuneList = tune.list)
   cs <- caretEnsemble(
     cl,
@@ -204,46 +166,18 @@ testthat::test_that("Ensembles using custom models work correctly", {
     )
   )
   testthat::expect_is(cs, "caretEnsemble")
-
-  # Validate names assigned to ensembled models
   testthat::expect_named(cs$models, c("custom.rf", "myrpart", "treebag"))
 
-  # Validate ensemble predictions
-  pred_stacked <- predict(cs) # stacked predictions
-  pred_in_sample <- predict(cs, newdata = X.class) # in sample predictions
-  pred_one <- predict(cs, newdata = X.class[2L, , drop = FALSE]) # one row predictions
-
-  # Check class
-  testthat::expect_s3_class(pred_stacked, "data.table")
-  testthat::expect_s3_class(pred_in_sample, "data.table")
-  testthat::expect_s3_class(pred_one, "data.table")
-
-  # Check rows
-  testthat::expect_identical(nrow(pred_stacked), 150L)
-  testthat::expect_identical(nrow(pred_in_sample), 150L)
-  testthat::expect_identical(nrow(pred_one), 1L)
-
-  # Check cols
-  testthat::expect_identical(ncol(pred_stacked), 2L)
-  testthat::expect_identical(ncol(pred_in_sample), 2L)
-  testthat::expect_identical(ncol(pred_one), 2L)
-
-  # stacked predcitons should be similar to in sample predictions
-  # These differ a lot!
-  testthat::expect_equal(pred_stacked, pred_in_sample, tol = 0.4)
+  test_predictions(cs, X.class, c(0.0198, 0.9802))
 
-  # One row predictions
-  testthat::expect_equivalent(pred_one$Yes, 0.07557944, tol = 0.1)
-  testthat::expect_equivalent(pred_one$No, 0.9244206, tol = 0.1)
-
-  # Verify that not specifying a method attribute for custom models causes an error
-  #  Add a custom caret model WITHOUT a properly assigned method attribute
-  tune.list <- list(
+  tune.list_bad <- list(
     caretModelSpec(method = getModelInfo("rf", regex = FALSE)[[1L]], tuneLength = 1L),
     treebag = caretModelSpec(method = "treebag", tuneLength = 1L)
   )
-  msg <- "Custom models must be defined with a \"method\" attribute"
-  testthat::expect_error(caretList(X.class, Y.class, tuneList = tune.list, trControl = train.control), regexp = msg)
+  testthat::expect_error(
+    caretList(X.class, Y.class, tuneList = tune.list_bad, trControl = train.control),
+    "Custom models must be defined with a \"method\" attribute"
+  )
 })
 
 testthat::test_that("Ensembles fails if predictions are not saved", {
@@ -254,3 +188,126 @@ testthat::test_that("Ensembles fails if predictions are not saved", {
     "No predictions saved during training. Please set savePredictions = 'final' in trainControl"
   )
 })
+
+######################################################################
+testthat::context("Variable importance and plotting")
+######################################################################
+
+testthat::test_that("caret::varImp.caretEnsemble works", {
+  set.seed(2239L)
+
+  for (m in list(ens.class, ens.reg)) {
+    for (s in c(TRUE, FALSE)) {
+      i <- caret::varImp(m, normalize = s)
+      testthat::expect_is(i, "numeric")
+      if (isClassifier(m)) {
+        len <- length(m$models) * 2L
+        n <- c(outer(c("rf", "glm", "rpart", "treebag"), c("No", "Yes"), paste, sep = "_"))
+        n <- matrix(n, ncol = 2L)
+        n <- c(t(n))
+      } else {
+        len <- length(m$models)
+        n <- names(m$models)
+      }
+      testthat::expect_length(i, len)
+      testthat::expect_named(i, n)
+      if (s) {
+        testthat::expect_true(all(i >= 0.0))
+        testthat::expect_true(all(i <= 1.0))
+        testthat::expect_equal(sum(i), 1.0, tolerance = 1e-6)
+      }
+    }
+  }
+})
+
+testthat::test_that("plot.caretEnsemble works", {
+  for (ens in list(ens.class, ens.reg)) {
+    plt <- plot(ens)
+    testthat::expect_is(plt, "ggplot")
+    testthat::expect_identical(nrow(plt$data), 5L)
+    testthat::expect_named(ens$models, plt$data$model_name[-1L])
+  }
+})
+
+testthat::test_that("ggplot2::autoplot.caretEnsemble works", {
+  for (ens in list(ens.class, ens.reg)) {
+    plt1 <- ggplot2::autoplot(ens)
+    plt2 <- ggplot2::autoplot(ens, xvars = c("Petal.Length", "Petal.Width"))
+
+    testthat::expect_is(plt1, "ggplot")
+    testthat::expect_is(plt2, "ggplot")
+    testthat::expect_is(plt1, "patchwork")
+    testthat::expect_is(plt2, "patchwork")
+
+    train_model <- ens.reg$models[[1L]]
+    testthat::expect_error(ggplot2::autoplot(train_model), "Objects of class (.*?) are not supported by autoplot")
+  }
+})
+
+testthat::test_that("summary.caretEnsemble works", {
+  for (ens in list(ens.class, ens.reg)) {
+    smry <- testthat::expect_silent(summary(ens.class))
+    testthat::expect_output(print(smry), ens.class$ens_model$metric)
+    for (name in names(ens.class$models)) {
+      testthat::expect_output(print(smry), name)
+    }
+  }
+})
+
+testthat::test_that("predict.caretEnsemble works with and without se and weights", {
+  for (ens in list(ens.class, ens.reg)) {
+    is_class <- isClassifier(ens)
+    for (se in c(FALSE, TRUE)) {
+      p <- predict(
+        ens,
+        newdata = X.reg,
+        se = se,
+        excluded_class_id = 1L
+      )
+      testthat::expect_s3_class(p, "data.table")
+      if (se) {
+        testthat::expect_named(p, c("pred", "lwr", "upr"))
+      } else {
+        testthat::expect_named(p, ifelse(is_class, "Yes", "pred"))
+      }
+    }
+  }
+})
+
+testthat::test_that("We can train and ensemble models with custom tuning lists", {
+  target <- "Class"
+
+  custom_list <- caretList(
+    x = Sonar[, setdiff(names(Sonar), target)],
+    y = Sonar[, target],
+    tuneList = list(
+      rpart = caretModelSpec(
+        method = "rpart",
+        tuneGrid = data.table::data.table(.cp = c(0.01, 0.001, 0.1, 1.0))
+      ),
+      knn = caretModelSpec(
+        method = "knn",
+        tuneLength = 9L
+      ),
+      lda = caretModelSpec(
+        method = "lda2",
+        tuneLength = 1L
+      ),
+      nnet = caretModelSpec(
+        method = "nnet",
+        tuneLength = 2L,
+        trace = FALSE,
+        softmax = FALSE
+      )
+    )
+  )
+  testthat::expect_is(custom_list, "caretList")
+  testthat::expect_identical(nrow(custom_list[["rpart"]]$results), 4L)
+  testthat::expect_identical(nrow(custom_list[["knn"]]$results), 9L)
+  testthat::expect_identical(nrow(custom_list[["lda"]]$results), 1L)
+  testthat::expect_identical(nrow(custom_list[["nnet"]]$results), 4L)
+  testthat::expect_false(custom_list[["nnet"]]$finalModel$softmax)
+
+  custom_ensemble <- caretEnsemble(custom_list)
+  testthat::expect_is(custom_ensemble, "caretEnsemble")
+})
diff --git a/tests/testthat/test-caretList.R b/tests/testthat/test-caretList.R
index cac7bece..8d2ce95e 100644
--- a/tests/testthat/test-caretList.R
+++ b/tests/testthat/test-caretList.R
@@ -1,13 +1,13 @@
-# Test caretList
+# Setup
 set.seed(442L)
 
-data(models.reg)
-data(X.reg)
-data(Y.reg)
+utils::data(models.reg)
+utils::data(X.reg)
+utils::data(Y.reg)
 
-data(models.class)
-data(X.class)
-data(Y.class)
+utils::data(models.class)
+utils::data(X.class)
+utils::data(Y.class)
 
 train <- caret::twoClassSim(
   n = 1000L, intercept = -8L, linearVars = 3L,
@@ -18,627 +18,106 @@ test <- caret::twoClassSim(
   noiseVars = 10L, corrVars = 4L, corrValue = 0.6
 )
 
-testthat::test_that("caretModelSpec returns valid specs", {
-  tuneList <- list(
-    rf1 = caretModelSpec(),
-    rf2 = caretModelSpec(method = "rf", tuneLength = 5L),
-    caretModelSpec(method = "rpart"),
-    caretModelSpec(method = "knn", tuneLength = 10L)
-  )
-  tuneList <- caretEnsemble::tuneCheck(tuneList)
-  testthat::expect_type(tuneList, "list")
-  testthat::expect_length(tuneList, 4L)
-  testthat::expect_identical(sum(duplicated(names(tuneList))), 0L)
-})
+n <- 100L
+p <- 1000L
+large_data <- list(
+  X = data.table::data.table(matrix(stats::rnorm(n * p), n, p)),
+  y = factor(sample(c("A", "B"), n, replace = TRUE))
+)
 
+################################################################
+testthat::context("caretModelSpec, tuneCheck, methodCheck")
+################################################################
 testthat::test_that("caretModelSpec and checking functions work as expected", {
   all_models <- sort(unique(caret::modelLookup()$model))
-  for (model in all_models) {
-    testthat::expect_identical(caretModelSpec(model, tuneLength = 5L, preProcess = "knnImpute")$method, model)
-  }
 
-  tuneList <- lapply(all_models, function(x) list(method = x, preProcess = "pca"))
-  all_models_check <- tuneCheck(tuneList)
-  testthat::expect_is(all_models_check, "list")
-  testthat::expect_length(all_models, length(all_models_check))
+  testthat::expect_identical(caretModelSpec("rf", tuneLength = 5L, preProcess = "knnImpute")$method, "rf")
 
   tuneList <- lapply(all_models, function(x) list(method = x, preProcess = "pca"))
-  names(tuneList) <- all_models
-  names(tuneList)[c(1L, 5L, 10L)] <- ""
   all_models_check <- tuneCheck(tuneList)
   testthat::expect_is(all_models_check, "list")
   testthat::expect_length(all_models, length(all_models_check))
 
   methodCheck(all_models)
-  err <- "The following models are not valid caret models: THIS_IS_NOT_A_REAL_MODEL"
-  testthat::expect_error(methodCheck(c(all_models, "THIS_IS_NOT_A_REAL_MODEL")), err)
-  testthat::expect_error(methodCheck(c(all_models, "THIS_IS_NOT_A_REAL_MODEL", "GBM")))
-})
-
-testthat::test_that("Target extraction functions work", {
-  data(iris)
-  testthat::expect_identical(extractCaretTarget(iris[, 1L:4L], iris[, 5L]), iris[, 5L])
-  testthat::expect_identical(extractCaretTarget(iris[, 2L:5L], iris[, 1L]), iris[, 1L])
-  testthat::expect_identical(extractCaretTarget(Species ~ ., iris), iris[, "Species"])
-  testthat::expect_identical(extractCaretTarget(Sepal.Width ~ ., iris), iris[, "Sepal.Width"])
-})
-
-testthat::test_that("caretList errors for bad models", {
-  data(iris)
-
-  # Basic checks
-  testthat::expect_error(caretList(Sepal.Width ~ ., iris), "Please either define a methodList or tuneList")
-  testthat::expect_warning(
-    caretList(Sepal.Width ~ ., iris, methodList = c("lm", "lm")),
-    "Duplicate entries in methodList. Using unique methodList values."
-  )
-  testthat::expect_is(caretList(Sepal.Width ~ ., iris, methodList = "lm", continue_on_fail = TRUE), "caretList")
-
-  # Check that by default a bad model kills the training job
-  bad <- list(
-    bad = caretModelSpec(method = "glm", tuneLength = 1L)
-  )
-  testthat::expect_output(
-    testthat::expect_warning(
-      testthat::expect_error(
-        caretList(iris[, 1L:4L], iris[, 5L], tuneList = bad),
-        regexp = "Stopping" # Stop training on the first error. This is the mssage straight from train.
-      ),
-      regexp = "model fit failed for Fold1"
-    ),
-    regexp = "Something is wrong; all the Accuracy metric values are missing:"
-  )
-  testthat::expect_output(
-    testthat::expect_warning(
-      testthat::expect_error(
-        caretList(iris[, 1L:4L], iris[, 5L], tuneList = bad, continue_on_fail = TRUE),
-        regexp = "caret:train failed for all models. Please inspect your data."
-      ),
-      regexp = "model fit failed for Fold1"
-    ),
-    regexp = "Something is wrong; all the Accuracy metric values are missing:"
-  )
-
-  # Check that at least one good model + continue_on_fail works
-  good_bad <- list(
-    good = caretModelSpec(method = "glmnet", tuneLength = 1L),
-    bad = caretModelSpec(method = "glm", tuneLength = 1L)
-  )
-  testthat::expect_s3_class(
-    testthat::expect_output(
-      testthat::expect_warning(
-        caretList(iris[, 1L:4L], iris[, 5L], tuneList = good_bad, continue_on_fail = TRUE),
-        regexp = "model fit failed for Fold1"
-      ),
-      regexp = "Something is wrong; all the Accuracy metric values are missing:"
-    ), "caretList"
-  )
-})
-
-testthat::test_that("caretList predictions", {
-  models <- testthat::expect_warning(
-    caretList(
-      iris[, 1L:2L], iris[, 3L],
-      tuneLength = 1L, verbose = FALSE,
-      methodList = "rf", tuneList = list(nnet = caretModelSpec(method = "nnet", trace = FALSE))
-    ), "There were missing values in resampled performance measures."
-  )
-
-  p1 <- predict(models)
-  p2 <- predict(models, newdata = iris[100L, 1L:2L])
-  p3 <- predict(models, newdata = iris[110L, 1L:2L])
-  testthat::expect_is(p1, "data.table")
-  testthat::expect_is(p1[[1L]], "numeric")
-  testthat::expect_is(p1[[2L]], "numeric")
-  testthat::expect_named(models, colnames(p1))
-  testthat::expect_is(p2, "data.table")
-  testthat::expect_is(p2[[1L]], "numeric")
-  testthat::expect_is(p2[[2L]], "numeric")
-  testthat::expect_named(models, colnames(p2))
-  testthat::expect_is(p3, "data.table")
-  testthat::expect_is(p3[[1L]], "numeric")
-  testthat::expect_is(p3[[2L]], "numeric")
-  testthat::expect_named(models, colnames(p3))
-
-  models <- caretList(
-    iris[, 1L:2L], iris[, 5L],
-    tuneLength = 1L, verbose = FALSE,
-    methodList = "rf",
-    tuneList = list(nnet = caretModelSpec(method = "nnet", trace = FALSE))
-  )
-
-  p2 <- predict(models, excluded_class_id = 0L)
-  p3 <- predict(models, newdata = iris[, 1L:2L], excluded_class_id = 0L)
-  testthat::expect_is(p2, "data.table")
-  testthat::expect_is(p2[[1L]], "numeric")
-  testthat::expect_is(p2[[2L]], "numeric")
-  testthat::expect_is(p2[[3L]], "numeric")
-  testthat::expect_is(p2[[4L]], "numeric")
-  testthat::expect_is(p3, "data.table")
-  testthat::expect_is(p3[[1L]], "numeric")
-  testthat::expect_is(p3[[2L]], "numeric")
-  testthat::expect_is(p3[[3L]], "numeric")
-  testthat::expect_is(p3[[4L]], "numeric")
-  testthat::expect_identical(
-    length(names(models)) * nlevels(as.factor(iris[, 5L])),
-    length(colnames(p3))
-  ) # check that we have the right number of columns
-  testthat::expect_identical(dim(p2), dim(p3))
-  testthat::expect_named(p2, names(p3))
-
-  modelnames <- names(models)
-  classes <- levels(iris[, 5L])
-  combinations <- expand.grid(classes, modelnames)
-  correct_colnames <- apply(combinations, 1L, function(x) paste(x[2L], x[1L], sep = "_"))
-  testthat::expect_named(
-    p3,
-    correct_colnames
-  ) # check the column names are correct and ordered correctly (methodname_classname)
-})
-
-testthat::test_that("as.caretList.list returns a caretList object", {
-  modelList <- caretList(Sepal.Length ~ Sepal.Width,
-    head(iris, 50L),
-    methodList = c("glm", "lm", "knn")
+  testthat::expect_error(
+    methodCheck(c(all_models, "THIS_IS_NOT_A_REAL_MODEL")),
+    "The following models are not valid caret models: THIS_IS_NOT_A_REAL_MODEL"
   )
-  class(modelList) <- "list"
-  testthat::expect_is(as.caretList(modelList), "caretList")
-})
 
-#############################################################
-testthat::context("Bad characters in target variable names and model names")
-#############################################################
-testthat::test_that("Target variable names with character | are not allowed", {
-  bad_iris <- iris[1L:100L, ]
-  bad_iris[, 5L] <- gsub("versicolor", "versicolor|1", bad_iris[, 5L], fixed = TRUE)
-  bad_iris[, 5L] <- gsub("setosa", "setosa|2", bad_iris[, 5L], fixed = TRUE)
-  bad_iris[, 5L] <- as.factor(as.character(bad_iris[, 5L]))
-
-  # Expect an error from caret
   testthat::expect_error(
-    caretList(
-      x = bad_iris[, -5L],
-      y = bad_iris[, 5L],
-      methodList = c("rpart", "glmnet")
-    ), "At least one of the class levels is not a valid R variable name; This will cause errors when class prob"
+    methodCheck("InvalidMethod"),
+    "The following models are not valid caret models: InvalidMethod"
   )
-})
 
-testthat::test_that("Character | in model names is transformed into a point", {
-  reduced_iris <- iris[1L:100L, ]
-  reduced_iris[, 5L] <- as.factor(as.character(reduced_iris[, 5L]))
-
-  # Chack that specified model names are transformed with function make.names
-  model_list <- caretList(
-    x = reduced_iris[, -5L],
-    y = reduced_iris[, 5L],
-    tuneList = list(
-      "nnet|1" = caretModelSpec(
-        method = "nnet",
-        tuneGrid = expand.grid(.size = c(1L, 3L), .decay = 0.3),
-        trace = FALSE
-      ),
-      "nnet|2" = caretModelSpec(
-        method = "nnet",
-        tuneGrid = expand.grid(.size = 3L, .decay = c(0.1, 0.3)),
-        trace = FALSE
-      )
-    )
-  )
-  testthat::expect_named(model_list, c("nnet.1", "nnet.2"))
-})
-
-###############################################
-testthat::context("We can fit models with a mix of methodList and tuneList")
-################################################
-testthat::test_that("We can fit models with a mix of methodList and tuneList", {
-  myList <- list(
-    rpart = caretModelSpec(method = "rpart", tuneLength = 10L),
-    rf = caretModelSpec(method = "rf", tuneGrid = data.table::data.table(mtry = 2L))
-  )
-  test <- testthat::expect_warning(
-    caretList(
-      x = iris[, 1L:3L],
-      y = iris[, 4L],
-      methodList = c("knn", "glm"),
-      tuneList = myList
-    ), "There were missing values in resampled performance measures."
+  testthat::expect_error(
+    methodCheck(list(invalid_method = 42L)),
+    "Method \"42\" is invalid"
   )
-  testthat::expect_is(test, "caretList")
-  testthat::expect_is(caretEnsemble(test), "caretEnsemble")
-  testthat::expect_length(test, 4L)
-  methods <- vapply(test, function(x) x$method, character(1L))
-  names(methods) <- NULL
-  testthat::expect_identical(methods, c("rpart", "rf", "knn", "glm"))
 })
 
-################################################
-testthat::context("We can handle different CV methods")
-################################################
-testthat::test_that("We can handle different CV methods", {
-  for (m in c(
-    "boot",
-    "adaptive_boot",
-    "cv",
-    "repeatedcv",
-    "adaptive_cv",
-    "LGOCV",
-    "adaptive_LGOCV"
-  )
-  ) {
-    N <- 7L
-    x <- iris[, 1L:3L]
-    y <- iris[, 4L]
-
-    if (m == "boot" || m == "adaptive_boot") {
-      idx <- caret::createResample(y, times = N, list = TRUE)
-    } else if (m == "cv" || m == "adaptive_cv") {
-      idx <- caret::createFolds(y, k = N, list = TRUE, returnTrain = TRUE)
-    } else if (m == "repeatedcv") {
-      idx <- caret::createMultiFolds(y, k = N, times = 2L)
-    } else if (m == "LGOCV" || m == "adaptive_LGOCV") {
-      idx <- caret::createDataPartition(
-        y,
-        times = N,
-        p = 0.5,
-        list = TRUE,
-        groups = min(5L, length(y))
-      )
-    }
-
-    models <- testthat::expect_warning(
-      caretList(
-        x = x,
-        y = y,
-        tuneLength = 2L,
-        methodList = c("rpart", "rf")
-      ), "There were missing values in resampled performance measures."
-    )
-    ens <- caretStack(models, method = "glm")
-
-    for (x in models) {
-      testthat::expect_s3_class(x, "train")
-    }
+################################################################
+testthat::context("S3 methods for caretlist")
+################################################################
 
-    ens <- caretEnsemble(models)
-
-    testthat::expect_is(ens, "caretEnsemble")
-
-    ens <- caretStack(models, method = "glm")
-
-    testthat::expect_is(ens, "caretStack")
-  }
-})
-
-testthat::test_that("Non standard cv methods work", {
+testthat::test_that("Target extraction functions work", {
   data(iris)
-  models <- lapply(
-    c("boot632", "LOOCV", "none"),
-    function(m) {
-      model <- caret::train(
-        x = iris[, 1L:2L],
-        y = iris[, 3L],
-        tuneLength = 1L,
-        data = iris,
-        method = "rf",
-        trControl = caret::trainControl(
-          method = m,
-          savePredictions = "final"
-        )
-      )
-      testthat::expect_is(model, "train")
-      model
-    }
-  )
-  caret_list <- as.caretList(models)
-  p <- predict(caret_list, newdata = iris[, 1L:2L])
-  testthat::expect_s3_class(p, "data.table")
-})
-
-###############################################
-testthat::context("Classification models")
-################################################
-testthat::test_that("Classification models", {
-  # Simple two method list
-  # Warning because we Are going to auto-set indexes
-  test1 <- caretList(
-    x = train[, -23L],
-    y = train[, "Class"],
-    methodList = c("knn", "glm")
-  )
-
-  testthat::expect_is(test1, "caretList")
-  testthat::expect_is(caretEnsemble(test1), "caretEnsemble")
-  testthat::expect_is(caretEnsemble(test1), "caretEnsemble")
-})
-
-testthat::test_that("Longer tests for Classification models", {
-  # Simple two method list
-  # Warning because we Are going to auto-set indexes
-  test1 <- caretList(
-    x = train[, -23L],
-    y = train[, "Class"],
-    methodList = c("knn", "glm")
-  )
-
-  testthat::expect_is(test1, "caretList")
-  testthat::expect_is(caretEnsemble(test1), "caretEnsemble")
-  testthat::expect_is(caretEnsemble(test1), "caretEnsemble")
-
-  test2 <- caretList(
-    x = train[, -23L],
-    y = train[, "Class"],
-    metric = "ROC",
-    methodList = c("knn", "glm", "rpart")
-  )
-
-  test3 <- caretList(
-    x = train[, -23L],
-    y = train[, "Class"],
-    metric = "ROC",
-    methodList = c("rpart", "knn", "glm")
-  )
-
-  testthat::expect_is(test2, "caretList")
-  testthat::expect_is(test3, "caretList")
-  testthat::expect_is(caretEnsemble(test2), "caretEnsemble")
-  testthat::expect_is(caretEnsemble(test3), "caretEnsemble")
-
-  testthat::expect_identical(test2[[1L]]$metric, "ROC")
-  testthat::expect_identical(test3[[1L]]$metric, "ROC")
-})
-
-testthat::test_that("Test that caretList preserves user specified error functions", {
-  test1 <- caretList(
-    x = train[, -23L],
-    y = train[, "Class"],
-    tuneLength = 7L,
-    methodList = c("knn", "rpart", "glm")
-  )
-
-  test2 <- caretList(
-    x = train[, -23L],
-    y = train[, "Class"],
-    tuneLength = 4L,
-    methodList = c("knn", "rpart", "glm")
-  )
-
-  testthat::expect_identical(test1[[1L]]$metric, "ROC")
-  testthat::expect_identical(test2[[1L]]$metric, "ROC")
-
-  testthat::expect_identical(nrow(test1[[1L]]$results), 7L)
-  testthat::expect_gt(nrow(test1[[1L]]$results), nrow(test2[[1L]]$results))
-  testthat::expect_identical(nrow(test2[[1L]]$results), 4L)
-
-  myEns2 <- caretEnsemble(test2)
-  myEns1 <- caretEnsemble(test1)
-  testthat::expect_is(myEns2, "caretEnsemble")
-  testthat::expect_is(myEns1, "caretEnsemble")
-
-  test1 <- caretList(
-    x = train[, -23L],
-    y = train[, "Class"],
-    tuneLength = 7L,
-    methodList = c("knn", "rpart", "glm")
-  )
-
-  test2 <- caretList(
-    x = train[, -23L],
-    y = train[, "Class"],
-    tuneLength = 4L,
-    methodList = c("knn", "rpart", "glm")
-  )
-
-  testthat::expect_identical(test1[[1L]]$metric, "ROC")
-  testthat::expect_identical(test2[[1L]]$metric, "ROC")
-
-  testthat::expect_identical(nrow(test1[[1L]]$results), 7L)
-  testthat::expect_gt(nrow(test1[[1L]]$results), nrow(test2[[1L]]$results))
-  testthat::expect_identical(nrow(test2[[1L]]$results), 4L)
-
-
-  myEns2 <- caretEnsemble(test2)
-  myEns1 <- caretEnsemble(test1)
-
-  testthat::expect_is(myEns2, "caretEnsemble")
-  testthat::expect_is(myEns1, "caretEnsemble")
+  testthat::expect_identical(extractCaretTarget(iris[, 1L:4L], iris[, 5L]), iris[, 5L])
+  testthat::expect_identical(extractCaretTarget(Species ~ ., iris), iris[, "Species"])
 })
 
-testthat::test_that("Users can pass a custom tuneList", {
-  tuneTest <- list(
-    rpart = caretModelSpec(
-      method = "rpart",
-      tuneGrid = data.table::data.table(.cp = c(0.01, 0.001, 0.1, 1.0))
-    ),
-    knn = caretModelSpec(
-      method = "knn",
-      tuneLength = 9L
-    ),
-    svmRadial = caretModelSpec(
-      method = "lda2",
-      tuneLength = 1L
-    )
-  )
-
-  test2a <- caretList(
-    x = train[, -23L],
-    y = train[, "Class"],
-    tuneList = tuneTest
-  )
-
-  myEns2a <- caretEnsemble(test2a)
-  testthat::expect_is(myEns2a, "caretEnsemble")
-  testthat::expect_is(test2a, "caretList")
-  testthat::expect_identical(nrow(test2a[[1L]]$results), 4L)
-  testthat::expect_identical(nrow(test2a[[2L]]$results), 9L)
-  testthat::expect_identical(nrow(test2a[[3L]]$results), 1L)
+testthat::test_that("[.caretList", {
+  subset_models <- models.class[1L:2L]
+  testthat::expect_s3_class(subset_models, "caretList")
+  testthat::expect_length(subset_models, 2L)
 })
 
-testthat::context("User tuneTest parameters are respected and model is ensembled")
-testthat::test_that("User tuneTest parameters are respected and model is ensembled", {
-  tuneTest <- list(
-    nnet = caretModelSpec(
-      method = "nnet",
-      tuneLength = 3L,
-      trace = FALSE,
-      softmax = FALSE
-    )
-  )
-  test <- caretList(
-    x = train[, -23L],
-    y = train[, "Class"],
-    tuneList = tuneTest
-  )
-  ens <- caretEnsemble(test)
-  testthat::expect_is(ens, "caretEnsemble")
-  testthat::expect_is(test, "caretList")
-  testthat::expect_identical(nrow(test[[1L]]$results), 3L * 3L)
-  testthat::expect_false(test[[1L]]$finalModel$softmax)
-})
+testthat::test_that("c.caretList", {
+  combined_models <- c(models.class, models.class)
+  testthat::expect_s3_class(combined_models, "caretList")
+  testthat::expect_length(combined_models, length(models.class) * 2L)
 
-testthat::context("Formula interface for caretList works")
-testthat::test_that("User tuneTest parameters are respected and model is ensembled", {
-  tuneTest <- list(
-    rpart = list(method = "rpart", tuneLength = 2L),
-    nnet = list(method = "nnet", tuneLength = 2L, trace = FALSE),
-    glm = list(method = "glm")
-  )
-  x <- iris[, 1L:3L]
-  y <- iris[, 4L]
-  set.seed(42L)
-  test_default <- testthat::expect_warning(
-    caretList(
-      x = x,
-      y = y,
-      tuneList = tuneTest
-    ), "There were missing values in resampled performance measures."
-  )
-  set.seed(42L)
-  test_flma <- testthat::expect_warning(
-    caretList(
-      y ~ .,
-      data = data.table::data.table(y = y, x),
-      tuneList = tuneTest
-    ), "There were missing values in resampled performance measures."
-  )
-  ens_default <- caretEnsemble(test_default)
-  ens_flma <- caretEnsemble(test_flma)
-  testthat::expect_is(ens_default, "caretEnsemble")
-  testthat::expect_is(ens_flma, "caretEnsemble")
+  combined_models <- c(models.class, models.class[[1L]])
+  testthat::expect_s3_class(combined_models, "caretList")
+  testthat::expect_length(combined_models, length(models.class) + 1L)
 
-  testthat::expect_equal(ens_default$RMSE, ens_flma$RMSE, tol = 0.000001)
-  testthat::expect_equal(ens_default$weights, ens_flma$weights, tol = 0.000001)
+  testthat::expect_error(c.caretList(list(a = 1L, b = 2L)), "class of modelList1 must be 'caretList' or 'train'")
 })
 
-###############################################
-testthat::context("Regression models")
-###############################################
+testthat::test_that("as.caretList", {
+  # Named
+  model_list <- list(model1 = models.class[[1L]], model2 = models.class[[2L]])
+  caretlist_object <- as.caretList(model_list)
+  testthat::expect_s3_class(caretlist_object, "caretList")
+  testthat::expect_length(caretlist_object, 2L)
 
-testthat::test_that("Regression Models", {
-  test1 <- caretList(
-    x = train[, c(-23L, -1L)],
-    y = train[, 1L],
-    methodList = c("glm", "lm")
-  )
-  test2 <- caretList(
-    x = train[, c(-23L, -1L)],
-    y = train[, 1L],
-    methodList = c("glm", "ppr", "lm")
-  )
+  # Unnamed
+  model_list <- list(models.class[[1L]], models.class[[2L]])
+  caretlist_object <- as.caretList(model_list)
+  testthat::expect_s3_class(caretlist_object, "caretList")
+  testthat::expect_length(caretlist_object, 2L)
 
-  ens1 <- caretEnsemble(test1)
-  ens2 <- caretEnsemble(test2)
-
-  testthat::expect_is(test1, "caretList")
-  testthat::expect_is(test2, "caretList")
-
-  testthat::expect_is(ens1, "caretEnsemble")
-  testthat::expect_is(ens2, "caretEnsemble")
-})
-
-testthat::test_that("methodCheck stops for invalid method type", {
-  testthat::expect_error(methodCheck(list(123L)), "Method \"123\" is invalid.")
-  testthat::expect_error(
-    methodCheck(list("invalid_method")),
-    "The following models are not valid caret models: invalid_method"
-  )
-})
-
-testthat::test_that("as.caretList stops for null object", {
+  # Error cases
   testthat::expect_error(as.caretList(NULL), "object is null")
-})
-
-testthat::test_that("as.caretList.list stops for non-list object", {
+  testthat::expect_error(as.caretList(1L), "object must be a list")
+  testthat::expect_error(as.caretList(list(1L)), "object requires all elements of list to be caret models")
+  testthat::expect_error(as.caretList(list(NULL)), "object requires all elements of list to be caret models")
   testthat::expect_error(as.caretList.list(1L), "object must be a list of caret models")
 })
 
-testthat::test_that("predict.caretList doesn't care about missing training data", {
-  new_model_list <- lapply(models.class, function(x) {
-    x$trainingData <- NULL
-    x
-  })
-  new_model_list <- as.caretList(new_model_list)
-  pred <- predict.caretList(new_model_list)
-  testthat::expect_is(pred, "data.table")
-  testthat::expect_identical(nrow(pred), 150L)
-  testthat::expect_named(pred, names(new_model_list))
-})
-
-testthat::test_that("extractModelName handles custom models correctly", {
-  mock_model <- list(method = list(method = "custom_method"))
-  class(mock_model) <- "train"
-  testthat::expect_identical(extractModelName(mock_model), "custom_method")
-})
-
-testthat::test_that("extractModelName handles custom models correctly", {
-  mock_model <- list(method = "custom", modelInfo = list(method = "custom_method"))
-  class(mock_model) <- "train"
-  testthat::expect_identical(extractModelName(mock_model), "custom_method")
-})
-
-testthat::test_that("as.caretList.list fails on NULL object", {
-  err <- "object requires all elements of list to be caret models"
-  testthat::expect_error(as.caretList(list(NULL)), err)
-})
-
-testthat::test_that("predict.caretList works when the progress bar is turned off", {
-  set.seed(42L)
-  N <- 100L
-  noise_level <- 1L / 10L
-  X <- data.table::data.table(
-    a = runif(N),
-    b = runif(N)
-  )
-  y <- 7.5 - 10.0 * X$a + 5.0 * X$b + noise_level * rnorm(N)
-  models <- caretList(
-    X, y,
-    tuneLength = 1L,
-    methodList = "lm"
-  )
-  pred <- predict(models, X, verbose = FALSE)[["lm"]]
-  rmse <- sqrt(mean((y - pred)^2L))
-  testthat::expect_lt(rmse, noise_level)
-})
-
-testthat::test_that("caretList handles missing data correctly", {
-  data(iris)
-  iris_with_na <- iris
-  x <- iris_with_na[, 1L:4L]
-  y <- iris_with_na[, 5L]
-  x[sample.int(nrow(x), 10L), sample.int(ncol(x), 2L)] <- NA
+################################################################
+testthat::context("predict.caretlist")
+################################################################
 
-  models <- caretList(
-    x = x,
-    y = y,
-    methodList = "rpart"
-  )
+testthat::test_that("predict.caretList works for classification and regression", {
+  class_preds <- predict(models.class, newdata = X.class, excluded_class_id = 0L)
+  reg_preds <- predict(models.reg, newdata = X.reg)
 
-  testthat::expect_s3_class(models, "caretList")
-  testthat::expect_length(models, 1L)
-})
+  testthat::expect_is(class_preds, "data.table")
+  testthat::expect_is(reg_preds, "data.table")
+  testthat::expect_identical(nrow(class_preds), nrow(X.class))
+  testthat::expect_identical(nrow(reg_preds), nrow(X.reg))
 
-testthat::test_that("caretList handles new factor levels in prediction", {
-  data(iris)
+  # Test for handling new factor levels in prediction
   idx <- seq_len(nrow(iris))
   idx_train <- sample(idx, 100L)
   idx_test <- setdiff(idx, idx_train)
@@ -656,79 +135,122 @@ testthat::test_that("caretList handles new factor levels in prediction", {
   pred <- predict(models, newdata = test_data)
   testthat::expect_is(pred, "data.table")
   testthat::expect_identical(nrow(pred), nrow(test_data))
+
+  # Test verbose option
+  p <- predict(models, newdata = test_data, verbose = TRUE)
+  testthat::expect_s3_class(p, "data.table")
+  testthat::expect_identical(nrow(p), nrow(test_data))
 })
 
-testthat::test_that("caretList handles large number of predictors", {
-  set.seed(123L)
-  n <- 100L
-  p <- 1000L
-  X <- data.table::data.table(matrix(rnorm(n * p), n, p))
-  y <- factor(sample(c("A", "B"), n, replace = TRUE))
+################################################################
+testthat::context("caretList")
+################################################################
 
-  models <- caretList(
-    x = X,
-    y = y,
-    methodList = c("glmnet", "rpart")
+testthat::test_that("caretList works for various scenarios", {
+  # Basic classification
+  test1 <- caretList(
+    x = train[, -23L],
+    y = train[, "Class"],
+    methodList = c("knn", "glm")
   )
+  testthat::expect_is(test1, "caretList")
+  testthat::expect_is(caretEnsemble(test1), "caretEnsemble")
 
-  testthat::expect_s3_class(models, "caretList")
-  testthat::expect_length(models, 2L)
-})
-
-testthat::test_that("caretList handles imbalanced data", {
-  set.seed(123L)
-  n <- 1000L
-  X <- data.table::data.table(x1 = rnorm(n), x2 = rnorm(n))
-  y <- factor(c(rep("A", 950L), rep("B", 50L)))
-
-  models <- caretList(
-    x = X,
-    y = y,
-    methodList = c("glmnet", "rpart")
+  # Regression
+  test_reg <- caretList(
+    x = train[, c(-23L, -1L)],
+    y = train[, 1L],
+    methodList = c("glm", "lm")
   )
+  testthat::expect_is(test_reg, "caretList")
+  testthat::expect_is(caretEnsemble(test_reg), "caretEnsemble")
 
+  # Handling missing data
+  iris_with_na <- iris
+  x <- iris_with_na[, 1L:4L]
+  y <- iris_with_na[, 5L]
+  x[sample.int(nrow(x), 10L), sample.int(ncol(x), 2L)] <- NA
+  models <- caretList(x = x, y = y, methodList = "rpart")
   testthat::expect_s3_class(models, "caretList")
-  testthat::expect_length(models, 2L)
-})
 
-
-testthat::test_that("caretList handles custom performance metrics", {
-  data(iris)
-  models <- caretList(
-    x = iris[, 1L:4L],
-    y = iris[, 5L],
-    metric = "default",
-    methodList = c("rpart", "rf"),
+  # Handling large number of predictors
+  models_large <- caretList(x = large_data$X, y = large_data$y, methodList = "rpart")
+  testthat::expect_s3_class(models_large, "caretList")
+  testthat::expect_length(models_large, 1L)
+
+  # Handling imbalanced data
+  imbalanced_y <- factor(c(rep("A", 95L), rep("B", 5L)))
+  testthat::expect_length(imbalanced_y, nrow(large_data$X))
+  models_imbalanced <- caretList(
+    x = large_data$X,
+    y = imbalanced_y,
+    methodList = "rpart",
     trControl = caret::trainControl(
       method = "cv",
-      number = 2L,
-      summaryFunction = function(data, lev = NULL, model = NULL) c(default = mean(data$obs == data$pred)),
-      allowParallel = FALSE,
-      classProbs = TRUE
+      classProbs = TRUE,
+      summaryFunction = twoClassSummary,
+      sampling = "up",
+      index = caret::createFolds(imbalanced_y, k = 5L, returnTrain = TRUE)
     )
   )
-  testthat::expect_s3_class(models, "caretList")
-  testthat::expect_true(all(vapply(models, function(m) "default" %in% colnames(m$results), logical(1L))))
-})
+  testthat::expect_s3_class(models_imbalanced, "caretList")
+  testthat::expect_length(models_imbalanced, 1L)
 
-###############################################
-testthat::context("S3 methods")
-###############################################
+  # Test error cases
+  testthat::expect_error(caretList(Sepal.Width ~ ., iris), "Please either define a methodList or tuneList")
+  testthat::expect_warning(
+    caretList(Sepal.Width ~ ., iris, methodList = c("lm", "lm")),
+    "Duplicate entries in methodList. Using unique methodList values."
+  )
 
-testthat::test_that("plot.caretList", {
+  # Test continue_on_fail
+  bad <- list(
+    bad = caretModelSpec(method = "glm", tuneLength = 1L)
+  )
+  testthat::expect_output(
+    testthat::expect_warning(
+      testthat::expect_error(
+        caretList(iris[, 1L:4L], iris[, 5L], tuneList = bad, continue_on_fail = TRUE),
+        regexp = "caret:train failed for all models. Please inspect your data."
+      ),
+      regexp = "model fit failed for Fold1"
+    ),
+    regexp = "Something is wrong; all the Accuracy metric values are missing:"
+  )
+})
+
+# Test plot and summary methods
+testthat::test_that("plot.caretList and summary.caretList work", {
   for (model_list in list(models.reg, models.class)) {
     plt <- plot(model_list)
     testthat::expect_is(plt, "ggplot")
     testthat::expect_identical(nrow(plt$data), 4L)
     testthat::expect_named(model_list, plt$data$model_name)
-  }
-})
 
-testthat::test_that("summary.caretList", {
-  for (model_list in list(models.reg, models.class)) {
     smry <- testthat::expect_silent(summary(model_list))
     for (name in names(model_list)) {
       testthat::expect_output(print(smry), name)
     }
   }
 })
+
+# Test combined regression, binary, multiclass models
+testthat::test_that("caretList supports combined regression, binary, multiclass", {
+  set.seed(42L)
+
+  reg_models <- caretList(Sepal.Length ~ Sepal.Width, iris, methodList = c("glm", "lm"))
+  bin_models <- caretList(factor(ifelse(Species == "setosa", "Yes", "No")) ~ Sepal.Width, iris,
+    methodList = c("lda", "rpart")
+  )
+  multi_models <- caretList(Species ~ Sepal.Width, iris, methodList = "rpart")
+
+  all_models <- c(reg_models, bin_models, multi_models)
+  testthat::expect_s3_class(all_models, "caretList")
+
+  stacked_p <- predict(all_models)
+  new_p <- predict(all_models, newdata = iris[1L:10L, ])
+  testthat::expect_is(stacked_p, "data.table")
+  testthat::expect_is(new_p, "data.table")
+  testthat::expect_identical(nrow(stacked_p), nrow(iris))
+  testthat::expect_identical(nrow(new_p), 10L)
+})
diff --git a/tests/testthat/test-caretPredict.R b/tests/testthat/test-caretPredict.R
index 5cf0eb38..2463c580 100644
--- a/tests/testthat/test-caretPredict.R
+++ b/tests/testthat/test-caretPredict.R
@@ -1,10 +1,28 @@
-data(models.reg)
-data(X.reg)
-data(Y.reg)
+# Setup
+utils::data(models.reg)
+utils::data(X.reg)
+utils::data(Y.reg)
+utils::data(models.class)
+utils::data(X.class)
+utils::data(Y.class)
 
-data(models.class)
-data(X.class)
-data(Y.class)
+set.seed(1234L)
+
+ens.reg <- caretEnsemble(
+  models.reg,
+  trControl = caret::trainControl(method = "cv", number = 2L, savePredictions = "final")
+)
+
+ens.class <- caretEnsemble(
+  models.class,
+  metric = "ROC",
+  trControl = caret::trainControl(
+    number = 2L,
+    summaryFunction = caret::twoClassSummary,
+    classProbs = TRUE,
+    savePredictions = TRUE
+  )
+)
 
 mod <- caret::train(
   X.reg,
@@ -13,15 +31,162 @@ mod <- caret::train(
   trControl = caret::trainControl(method = "none")
 )
 
-testthat::test_that("Extracting metrics works if there is no SD", {
-  # In the case of no resampling, metrics will not have an SD to extract
+# Helper function for testing
+expect_data_table_structure <- function(dt, expected_names) {
+  testthat::expect_s3_class(dt, "data.table")
+  testthat::expect_named(dt, expected_names)
+}
+
+#############################################################################
+testthat::context("caretPredict and extractMetric")
+#############################################################################
+testthat::test_that("caretPredict extracts best predictions correctly", {
+  stacked_preds_class <- caretPredict(models.class[[1L]], excluded_class_id = 0L)
+  stacked_preds_reg <- caretPredict(models.reg[[1L]])
+
+  expect_data_table_structure(stacked_preds_class, c("No", "Yes"))
+  expect_data_table_structure(stacked_preds_reg, "pred")
+})
+
+testthat::test_that("extractMetric works for different model types", {
+  # Test for model with no resampling (no SD)
   metric <- extractMetric(mod)
-  expect_s3_class(metric, "data.table")
-  expect_named(metric, c("model_name", "metric", "value", "sd"))
-  expect_is(metric$model_name, "character")
-  expect_is(metric$metric, "character")
-  expect_is(metric$value, "numeric")
-  expect_is(metric$sd, "numeric")
-  testthat::expect_true(is.na(metric$value))
-  testthat::expect_true(is.na(metric$sd))
+  expect_data_table_structure(metric, c("model_name", "metric", "value", "sd"))
+  testthat::expect_true(is.na(metric$value), is.na(metric$sd))
+
+  # Test for ensemble models
+  for (ens in list(ens.class, ens.reg)) {
+    metrics <- extractMetric(ens)
+    expect_data_table_structure(metrics, c("model_name", "metric", "value", "sd"))
+    testthat::expect_named(ens$models, metrics$model_name[-1L])
+  }
+})
+
+#############################################################################
+testthat::context("S3 methods and model operations")
+#############################################################################
+testthat::test_that("c.train on 2 train objects", {
+  testthat::expect_error(c.train(list()), "class of modelList1 must be 'caretList' or 'train'")
+
+  combined_models <- c(models.class[[1L]], models.class[[1L]])
+  testthat::expect_s3_class(combined_models, "caretList")
+  testthat::expect_length(combined_models, 2L)
+  testthat::expect_identical(anyDuplicated(names(combined_models)), 0L)
+  testthat::expect_length(unique(names(combined_models)), 2L)
+})
+
+testthat::test_that("c.train on a train and a caretList", {
+  bigList <- c(models.reg[[1L]], models.class)
+  testthat::expect_is(bigList, "caretList")
+  testthat::expect_identical(anyDuplicated(names(bigList)), 0L)
+  testthat::expect_length(unique(names(bigList)), 5L)
+})
+
+testthat::test_that("extractModelName handles different model types", {
+  testthat::expect_identical(extractModelName(models.class[[1L]]), "rf")
+  testthat::expect_identical(extractModelName(models.reg[[1L]]), "rf")
+
+  custom_model <- models.class[[1L]]
+  custom_model$method <- list(method = "custom_rf")
+  testthat::expect_identical(extractModelName(custom_model), "custom_rf")
+
+  mock_model <- list(method = list(method = "custom_method"))
+  class(mock_model) <- "train"
+  testthat::expect_identical(extractModelName(mock_model), "custom_method")
+
+  mock_model <- list(method = "custom", modelInfo = list(method = "custom_method"))
+  class(mock_model) <- "train"
+  testthat::expect_identical(extractModelName(mock_model), "custom_method")
+})
+
+#############################################################################
+testthat::context("isClassifierAndValidate")
+#############################################################################
+testthat::test_that("isClassifierAndValidate handles various model types", {
+  models_multi <- caretList(
+    iris[, 1L:2L], iris[, 5L],
+    tuneLength = 1L, verbose = FALSE,
+    methodList = c("rf", "gbm")
+  )
+  models_multi_bin_reg <- c(models_multi, models.class, models.reg)
+  testthat::expect_is(vapply(models_multi_bin_reg, isClassifierAndValidate, logical(1L)), "logical")
+
+  # Test when predictions are missing
+  model_list <- models.class
+  model_list[[1L]]$pred <- NULL
+  testthat::expect_is(vapply(model_list, isClassifierAndValidate, logical(1L)), "logical")
+  testthat::expect_equivalent(unique(vapply(model_list, isClassifierAndValidate, logical(1L))), TRUE)
+
+  # Test error cases
+  model_list <- models.class
+  model_list[[1L]]$modelInfo$prob <- FALSE
+  testthat::expect_error(
+    lapply(model_list, isClassifierAndValidate),
+    "No probability function found. Re-fit with a method that supports prob."
+  )
+
+  model_list <- models.class
+  model_list[[1L]]$control$classProbs <- FALSE
+  testthat::expect_error(
+    lapply(model_list, isClassifierAndValidate, validate_for_stacking = TRUE),
+    "classProbs = FALSE. Re-fit with classProbs = TRUE in trainControl."
+  )
+
+  # Test for non-caretList object
+  testthat::expect_error(
+    isClassifierAndValidate(list(model = lm(Y.reg ~ ., data = as.data.frame(X.reg)))),
+    "is(object, \"train\") is not TRUE",
+    fixed = TRUE
+  )
+
+  # Test for models without savePredictions
+  model <- models.class[[1L]]
+  model$control$savePredictions <- NULL
+  testthat::expect_error(
+    isClassifierAndValidate(model),
+    "Must have savePredictions = 'all', 'final', or TRUE in trainControl to do stacked predictions."
+  )
+  model$control$savePredictions <- "BAD_VALUE"
+  testthat::expect_error(
+    isClassifierAndValidate(model),
+    "Must have savePredictions = 'all', 'final', or TRUE in trainControl to do stacked predictions."
+  )
+})
+
+#############################################################################
+testthat::context("validateExcludedClass")
+#############################################################################
+testthat::test_that("validateExcludedClass handles various inputs", {
+  testthat::expect_error(validateExcludedClass("invalid"), "classification excluded level must be numeric: invalid")
+  testthat::expect_warning(
+    testthat::expect_error(validateExcludedClass(Inf), "classification excluded level must be finite: Inf"),
+    "classification excluded level is not an integer: Inf"
+  )
+  testthat::expect_warning(
+    testthat::expect_error(validateExcludedClass(-1.0), "classification excluded level must be >= 0: -1"),
+    "classification excluded level is not an integer:"
+  )
+  testthat::expect_warning(validateExcludedClass(1.1), "classification excluded level is not an integer: 1.1")
+  testthat::expect_identical(validateExcludedClass(3L), 3L)
+
+  # Edge cases
+  testthat::expect_identical(validateExcludedClass(0L), 0L)
+  testthat::expect_identical(validateExcludedClass(1L), 1L)
+  testthat::expect_identical(validateExcludedClass(4L), 4L)
+  w <- "classification excluded level is not an integer:"
+  testthat::expect_warning(testthat::expect_identical(validateExcludedClass(0.0), 0L), w)
+  testthat::expect_warning(testthat::expect_identical(validateExcludedClass(1.0), 1L), w)
+  testthat::expect_warning(testthat::expect_identical(validateExcludedClass(4.0), 4L), w)
+  testthat::expect_error(validateExcludedClass(-1L), "classification excluded level must be >= 0: -1")
+
+  # Additional tests
+  testthat::expect_warning(validateExcludedClass(NULL), "No excluded_class_id set. Setting to 1L.")
+  testthat::expect_error(
+    validateExcludedClass(c(1L, 2L)),
+    "classification excluded level must have a length of 1: length=2"
+  )
+  testthat::expect_warning(
+    testthat::expect_error(validateExcludedClass(-0.000001), "classification excluded level must be >= 0: -1e-06"),
+    "classification excluded level is not an integer"
+  )
 })
diff --git a/tests/testthat/test-caretStack.R b/tests/testthat/test-caretStack.R
index ad127f5a..8f8eac2f 100644
--- a/tests/testthat/test-caretStack.R
+++ b/tests/testthat/test-caretStack.R
@@ -1,85 +1,85 @@
-data(models.reg)
-data(X.reg)
-data(Y.reg)
-data(models.class)
-data(X.class)
-data(Y.class)
-
-ens.class <- caretStack(
-  models.class,
-  method = "glm",
-  preProcess = "pca",
-  trControl = caret::trainControl(method = "cv", number = 2L, savePredictions = "final", classProbs = TRUE)
+# Load data and create models
+utils::data(models.reg)
+utils::data(X.reg)
+utils::data(Y.reg)
+utils::data(models.class)
+utils::data(X.class)
+utils::data(Y.class)
+utils::data(iris)
+
+models_multiclass <- caretList(
+  x = iris[, -5L],
+  y = iris[, 5L],
+  methodList = c("rpart", "glmnet")
 )
 
-ens.reg <- caretStack(
-  models.reg,
-  method = "lm",
-  preProcess = "pca",
-  trControl = caret::trainControl(method = "cv", number = 2L, savePredictions = "final", classProbs = FALSE)
+control_class <- caret::trainControl(
+  method = "cv",
+  number = 2L,
+  savePredictions = "final",
+  classProbs = TRUE,
+  summaryFunction = twoClassSummary,
+  index = caret::createFolds(Y.class, 2L)
 )
+ens.class <- caretStack(models.class, preProcess = "pca", method = "glm", metric = "ROC", trControl = control_class)
+
+control_reg <- caret::trainControl(
+  method = "cv",
+  number = 2L,
+  savePredictions = "final",
+  classProbs = FALSE,
+  index = caret::createFolds(Y.reg, 2L)
+)
+ens.reg <- caretStack(models.reg, preProcess = "pca", method = "lm", trControl = control_reg)
+
+# Helper functions
+expect_all_finite <- function(x) {
+  testthat::expect_true(all(vapply(x, function(col) all(is.finite(col)), logical(1L))))
+}
+
+######################################################################
+testthat::context("caretStack")
+######################################################################
+
+testthat::test_that("caretStack creates valid ensemble models", {
+  testthat::expect_s3_class(ens.class, "caretStack")
+  testthat::expect_s3_class(ens.reg, "caretStack")
 
-testthat::context("Does stacking and prediction work?")
+  testthat::expect_s3_class(summary(ens.class), "summary.caretStack")
+  testthat::expect_s3_class(plot(ens.class), "ggplot")
+  testthat::expect_output(print(ens.class), "The following models were ensembled: rf, glm, rpart, treebag")
+})
 
-testthat::test_that("We can make predictions from stacks, including cases where the stacked model has preprocessing", {
+testthat::test_that("predict works for classification and regression ensembles", {
   ens_list <- list(class = ens.class, reg = ens.reg)
   X_list <- list(class = X.class, reg = X.reg)
 
   for (model_name in names(ens_list)) {
-    # S3 methods
     ens <- ens_list[[model_name]]
-    testthat::expect_s3_class(ens, "caretStack")
-    testthat::expect_s3_class(summary(ens), "summary.caretStack")
-    testthat::expect_s3_class(plot(ens), "ggplot")
-    invisible(capture.output(print(ens)))
+    X <- X_list[[model_name]]
 
-    # Predictions
     param_grid <- expand.grid(
       se = c(TRUE, FALSE),
       newdata = c(TRUE, FALSE)
     )
+
     for (i in seq_len(nrow(param_grid))) {
       params <- param_grid[i, ]
-      X <- X_list[[model_name]]
-      expected_rows <- nrow(X)
-      newdata <- NULL
-      if (params$newdata) {
-        newdata <- X[1L:50L, ]
-        expected_rows <- 50L
-      }
+      newdata <- if (params$newdata) X[1L:50L, ] else NULL
+      expected_rows <- if (params$newdata) 50L else nrow(X)
+
       pred <- predict(ens, newdata = newdata, se = params$se)
       testthat::expect_s3_class(pred, "data.table")
       testthat::expect_identical(nrow(pred), expected_rows)
-      testthat::expect_true(all(vapply(pred, is.numeric, logical(1L))))
+      expect_all_finite(pred)
     }
   }
 })
 
-testthat::test_that("For classificaiton, we can predict the class labels", {
+testthat::test_that("Classification predictions return correct output", {
   pred.class <- predict(ens.class, X.class, return_class_only = TRUE)
-  expect_is(pred.class, "factor")
-  expect_length(pred.class, nrow(X.class))
-})
-
-testthat::test_that("caretStack plots", {
-  ens.gbm <- caretStack(
-    models.reg,
-    method = "gbm", tuneLength = 2L, verbose = FALSE
-  )
-  testthat::expect_s3_class(ens.gbm, "caretStack")
-
-  plt <- plot(ens.gbm)
-  expect_s3_class(plt, "ggplot")
-
-  dotplot <- lattice::dotplot(ens.gbm, metric = "RMSE")
-  expect_s3_class(dotplot, "trellis")
-})
-
-testthat::test_that("Prediction names are correct with SE", {
-  testthat::expect_named(
-    predict(ens.reg, X.reg, se = TRUE, excluded_class_id = 0L),
-    c("pred", "lwr", "upr")
-  )
+  testthat::expect_s3_class(pred.class, "factor")
+  testthat::expect_length(pred.class, nrow(X.class))
 
   testthat::expect_named(
     predict(ens.class, X.class, se = TRUE, excluded_class_id = 1L),
@@ -92,9 +92,14 @@ testthat::test_that("Prediction names are correct with SE", {
   )
 })
 
-testthat::test_that("Prediction equivalence", {
-  # Note that SE is stochastic, since it uses permutation importance
+testthat::test_that("Regression predictions return correct output", {
+  testthat::expect_named(
+    predict(ens.reg, X.reg, se = TRUE, excluded_class_id = 0L),
+    c("pred", "lwr", "upr")
+  )
+})
 
+testthat::test_that("Predictions are reproducible", {
   set.seed(42L)
   p1 <- predict(ens.class, X.class, se = TRUE, level = 0.8)
   set.seed(42L)
@@ -103,153 +108,51 @@ testthat::test_that("Prediction equivalence", {
   testthat::expect_equivalent(p1, p2)
 })
 
-testthat::test_that("Test na.action pass through", {
-  set.seed(1337L)
-
-  # drop the first model because it does not support na.pass
-  ens.reg <- caretStack(models.reg[2L:3L], method = "lm")
+testthat::test_that("caretStack handles missing data correctly", {
+  ens.reg.subset <- caretEnsemble::caretStack(models.reg[2L:3L], method = "lm")
 
   X_reg_na <- X.reg
-  # introduce random NA values into a column
   X_reg_na[sample.int(nrow(X_reg_na), 20L), sample.int(ncol(X_reg_na) - 1L, 1L)] <- NA
 
-  pred.reg <- predict(ens.reg, newdata = X_reg_na, na.action = na.pass)
+  pred.reg <- predict(ens.reg.subset, newdata = X_reg_na)
   testthat::expect_identical(nrow(pred.reg), nrow(X_reg_na))
 
-  pred.reg <- predict(ens.reg, newdata = X_reg_na)
-  testthat::expect_false(nrow(pred.reg) != nrow(X_reg_na))
+  pred.reg <- predict(ens.reg.subset, newdata = X_reg_na)
+  testthat::expect_identical(nrow(pred.reg), nrow(X_reg_na))
 })
 
-testthat::test_that("predict.caretStack works correctly if the multiclass excluded level is too high", {
-  data(iris)
-
-  # Create a caretList
-  model_list <- caretList(
-    Species ~ .,
-    data = iris,
-    methodList = c("rpart", "rf")
-  )
-
-  # Make sure predictions still work if the exlcuded level is too high
-  meta_model <- caretStack(
-    model_list,
-    method = "rpart",
-    excluded_class_id = 4L
-  )
+testthat::test_that("caretStack handles multiclass problems", {
+  meta_model <- caretEnsemble::caretStack(models_multiclass, method = "rpart", excluded_class_id = 4L)
   pred <- predict(meta_model, newdata = iris)
   testthat::expect_identical(nrow(pred), 150L)
   testthat::expect_identical(ncol(pred), 3L)
-  all_finite <- function(x) all(is.finite(x))
-  testthat::expect_true(all(vapply(pred, all_finite, logical(1L))))
+  expect_all_finite(pred)
 })
 
-testthat::context("caretStack edge cases")
-
-testthat::test_that("caretStack handles different stacking algorithms", {
-  for (x in list(list(models.reg, X.reg), list(models.class, X.class))) {
-    model_list <- x[[1L]]
-    test_data <- x[[2L]]
+testthat::test_that("caretStack works with different stacking algorithms", {
+  stack_methods <- c("glm", "rf", "gbm", "glmnet")
 
-    stack_methods <- c("glm", "rf", "gbm", "glmnet")
-
-    for (method in stack_methods) {
-      if (method == "gbm") {
-        stack <- caretStack(
-          model_list,
-          method = method,
-          verbose = FALSE
-        )
+  for (method in stack_methods) {
+    for (model_list in list(models.reg, models.class)) {
+      stack <- if (method == "gbm") {
+        caretEnsemble::caretStack(model_list, method = method, verbose = FALSE)
       } else {
-        stack <- caretStack(
-          model_list,
-          method = method
-        )
+        caretEnsemble::caretStack(model_list, method = method)
       }
 
       testthat::expect_s3_class(stack, "caretStack")
       testthat::expect_identical(stack$ens_model$method, method)
 
-      predictions <- predict(stack, newdata = test_data)
-      testthat::expect_identical(nrow(predictions), nrow(test_data))
+      predictions <- predict(stack, newdata = if (identical(model_list, models.reg)) X.reg else X.class)
+      testthat::expect_identical(nrow(predictions), nrow(if (identical(model_list, models.reg)) X.reg else X.class))
     }
   }
 })
 
-testthat::test_that("caretStack handles missing data in new data", {
-  models.class.subset <- models.class[c("rpart", "treebag")]
-
-  stack <- caretStack(
-    models.class.subset,
-    method = "rpart"
-  )
-
-  test_data_with_na <- X.class
-  test_data_with_na[1L:5L, 1L] <- NA
-
-  pred <- predict(stack, newdata = test_data_with_na)
-  testthat::expect_identical(nrow(pred), nrow(test_data_with_na))
-})
-
-testthat::test_that("caretStack handles different metrics", {
-  metrics <- c("ROC", "Sens", "Spec")
-  for (metric in metrics) {
-    stack <- caretStack(
-      models.class,
-      method = "glm",
-      metric = metric,
-      trControl = caret::trainControl(
-        method = "cv",
-        number = 3L,
-        classProbs = TRUE,
-        summaryFunction = caret::twoClassSummary
-      )
-    )
-    testthat::expect_s3_class(stack, "caretStack")
-    testthat::expect_identical(stack$ens_model$metric, metric)
-  }
-})
-
-testthat::test_that("caretStack handles upsampling data", {
-  data(iris)
-  train_data <- iris
-
-  imbalanced_data <- rbind(
-    train_data[train_data$Species == "setosa", ],
-    train_data[train_data$Species == "versicolor", ][1L:10L, ],
-    train_data[train_data$Species == "virginica", ][1L:5L, ]
-  )
-
-  model_list <- caretList(
-    x = imbalanced_data[, 1L:4L],
-    y = imbalanced_data$Species,
-    methodList = "rpart",
-    trControl = caret::trainControl(
-      method = "cv",
-      number = 3L,
-      classProbs = TRUE,
-      sampling = "up",
-      savePredictions = "final"
-    )
-  )
-
-  stack <- caretStack(
-    model_list,
-    method = "rpart"
-  )
-
-  testthat::expect_s3_class(stack, "caretStack")
-  pred <- predict(stack, newdata = imbalanced_data)
-  testthat::expect_identical(nrow(pred), nrow(imbalanced_data))
-})
-
 testthat::test_that("caretStack handles custom preprocessing", {
   preprocess <- c("center", "scale", "pca")
   for (model_list in list(models.class, models.reg)) {
-    stack <- caretStack(
-      model_list,
-      method = "glm",
-      preProcess = preprocess
-    )
+    stack <- caretEnsemble::caretStack(model_list, method = "glm", preProcess = preprocess)
     testthat::expect_s3_class(stack, "caretStack")
     testthat::expect_named(stack$ens_model$preProcess$method, c(preprocess, "ignore"))
   }
@@ -261,7 +164,7 @@ testthat::test_that("caretStack handles custom performance function", {
   }
 
   for (model_list in list(models.class, models.reg)) {
-    stack <- caretStack(
+    stack <- caretEnsemble::caretStack(
       model_list,
       method = "glm",
       metric = "default",
@@ -273,107 +176,167 @@ testthat::test_that("caretStack handles custom performance function", {
   }
 })
 
-testthat::test_that("predict.caretStack works if excluded_class_id is not set", {
-  ens <- caretStack(models.class)
-  ens[["excluded_class_id"]] <- NULL
-  pred <- testthat::expect_warning(predict(ens, X.class), "No excluded_class_id set. Setting to 1L.")
-
-  # Note that we don't exclude the class from the ensemble predictions, but merely from the preprocessing
-  testthat::expect_s3_class(pred, "data.table") # caret returns data.frame
-  testthat::expect_identical(nrow(pred), nrow(X.class))
-  testthat::expect_identical(ncol(pred), 2L)
-  testthat::expect_named(pred, c("No", "Yes"))
-})
-
-testthat::context("Edge cases")
-
-testthat::test_that("caretStack coerces lists to caretLists", {
-  model_list <- models.reg
-  class(model_list) <- "list"
-  names(model_list) <- NULL
-  ens <- testthat::expect_warning(
-    caretStack(model_list),
-    "Attempting to coerce all.models to a caretList."
-  )
-  testthat::expect_s3_class(ens, "caretStack")
-  testthat::expect_s3_class(ens$models, "caretList")
-  testthat::expect_named(ens$models, names(models.reg))
-})
-
-testthat::test_that("caretStack fails if new_X is NULL and newY is not and vice versa", {
-  err <- "Both new_X and new_y must be NULL, or neither."
-  testthat::expect_error(caretStack(models.reg, new_X = NULL, new_y = Y.reg), err)
-  testthat::expect_error(caretStack(models.reg, new_X = X.reg, new_y = NULL), err)
-})
-
-testthat::test_that("caretStack works if both new_X and new_Y are supplied", {
+testthat::test_that("caretStack handles new data correctly", {
   set.seed(42L)
   N <- 50L
   idx <- sample.int(nrow(X.reg), N)
+
   stack_class <- caretStack(
     models.class,
+    metric = "ROC",
+    method = "rpart",
     new_X = X.class[idx, ],
     new_y = Y.class[idx],
-    method = "rpart",
-    # Need probs for stacked preds
-    trControl = caret::trainControl(method = "cv", number = 2L, savePredictions = "final", classProbs = TRUE)
+    trControl = control_class
   )
+
   stack_reg <- caretStack(
     models.reg,
+    method = "glm",
     new_X = X.reg[idx, ],
     new_y = Y.reg[idx],
-    method = "glm",
-    # Need probs for stacked preds
-    trControl = caret::trainControl(method = "cv", number = 2L, savePredictions = "final", classProbs = FALSE)
+    trControl = control_reg
   )
 
   testthat::expect_s3_class(stack_class, "caretStack")
   testthat::expect_s3_class(stack_reg, "caretStack")
 
-  pred_class_stack <- predict(stack_class)
-  stack_reg_stack <- predict(stack_reg)
-
-  testthat::expect_s3_class(pred_class_stack, "data.table")
-  testthat::expect_s3_class(stack_reg_stack, "data.table")
-
-  testthat::expect_identical(nrow(pred_class_stack), N)
-  testthat::expect_identical(nrow(stack_reg_stack), N)
-
-  testthat::expect_identical(ncol(pred_class_stack), 2L)
-  testthat::expect_identical(ncol(stack_reg_stack), 1L)
-
-  pred_class <- predict(stack_class, new_X = X.class)
-  pred_reg <- predict(stack_reg, new_X = X.reg)
+  pred_class <- predict(stack_class, X.class)
+  pred_reg <- predict(stack_reg, X.reg)
 
   testthat::expect_s3_class(pred_class, "data.table")
   testthat::expect_s3_class(pred_reg, "data.table")
 
-  testthat::expect_identical(nrow(pred_class), N)
-  testthat::expect_identical(nrow(pred_reg), N)
+  testthat::expect_identical(nrow(pred_class), nrow(X.class))
+  testthat::expect_identical(nrow(pred_reg), nrow(X.reg))
 
   testthat::expect_identical(ncol(pred_class), 2L)
   testthat::expect_identical(ncol(pred_reg), 1L)
 })
 
-testthat::test_that("varImp works in for class and reg in sample", {
+testthat::test_that("caretStack coerces lists to caretLists", {
+  models <- list(
+    models.class[[1L]],
+    models.class[[2L]]
+  )
+  testthat::expect_warning(
+    caretStack(models, method = "glm", tuneLength = 1L),
+    "Attempting to coerce all.models to a caretList."
+  )
+})
+
+testthat::test_that("caretStack errors if new_X is provided but not new_y", {
+  testthat::expect_error(
+    caretStack(models.class, new_X = X.class),
+    "Both new_X and new_y must be NULL, or neither."
+  )
+})
+
+testthat::test_that("caretStack errors if new_y is provided but not new_X", {
+  testthat::expect_error(
+    caretStack(models.class, new_y = Y.class),
+    "Both new_X and new_y must be NULL, or neither."
+  )
+})
+
+######################################################################
+testthat::context("S3 methods for caretStack")
+######################################################################
+
+testthat::test_that("print", {
   for (ens in list(ens.class, ens.reg)) {
-    imp <- varImp(ens)
-    expect_is(imp, "numeric")
-    expect_named(imp, names(ens$models))
-    expect_equal(sum(imp), 1.0, tolerance = 1e-6)
+    testthat::expect_output(print(ens), "The following models were ensembled: rf, glm, rpart, treebag")
+    testthat::expect_output(print(ens), "150 samples")
+    testthat::expect_output(print(ens), "4 predictor")
   }
 })
 
-testthat::test_that("varImp works in for class on new data", {
-  imp <- varImp(ens.class, X.class)
-  expect_is(imp, "numeric")
-  expect_named(imp, names(ens.class$models))
-  expect_equal(sum(imp), 1.0, tolerance = 1e-6)
+testthat::test_that("summary", {
+  for (ens in list(ens.class, ens.reg)) {
+    s <- summary(ens)
+    testthat::expect_s3_class(s, "summary.caretStack")
+    testthat::expect_output(print(s), "The following models were ensembled: rf, glm, rpart, treebag")
+    testthat::expect_output(print(s), "Model Importance:")
+    testthat::expect_output(print(s), "Model accuracy:")
+  }
+})
+
+testthat::test_that("plot", {
+  for (ens in list(ens.class, ens.reg)) {
+    p <- plot(ens)
+    testthat::expect_s3_class(p, "ggplot")
+  }
+})
+
+testthat::test_that("dotplot", {
+  for (ens in list(ens.class, ens.reg)) {
+    p <- lattice::dotplot(ens)
+    testthat::expect_s3_class(p, "trellis")
+  }
+})
+
+testthat::test_that("autoplot", {
+  for (ens in list(ens.class, ens.reg)) {
+    p <- ggplot2::autoplot(ens)
+    testthat::expect_s3_class(p, "ggplot")
+  }
+})
+
+######################################################################
+testthat::context("varImp")
+######################################################################
+
+testthat::test_that("varImp works for classification and regression", {
+  for (ens in list(ens.class, ens.reg)) {
+    imp <- caret::varImp(ens)
+    testthat::expect_type(imp, "double")
+    testthat::expect_named(imp, names(ens$models))
+    testthat::expect_equal(sum(imp), 1.0, tolerance = 1e-6)
+
+    imp_new_data <- caret::varImp(ens, if (identical(ens, ens.class)) X.class else X.reg)
+    testthat::expect_type(imp_new_data, "double")
+    testthat::expect_named(imp_new_data, names(ens$models))
+    testthat::expect_equal(sum(imp_new_data), 1.0, tolerance = 1e-6)
+  }
 })
 
-testthat::test_that("varImp works in for reg on new data", {
-  imp <- varImp(ens.reg, X.class)
-  expect_is(imp, "numeric")
-  expect_named(imp, names(ens.reg$models))
-  expect_equal(sum(imp), 1.0, tolerance = 1e-6)
+######################################################################
+testthat::context("wtd.sd")
+######################################################################
+
+testthat::test_that("wtd.sd calculates weighted standard deviation correctly", {
+  x <- c(1L, 2L, 3L, 4L, 5L)
+  w <- c(1L, 1L, 1L, 1L, 1L)
+  testthat::expect_equal(caretEnsemble::wtd.sd(x, w), stats::sd(x), tolerance = 0.001)
+
+  w_uneven <- c(2L, 1L, 1L, 1L, 1L)
+  testthat::expect_false(isTRUE(all.equal(caretEnsemble::wtd.sd(x, w_uneven), stats::sd(x))))
+
+  x_na <- c(1L, 2L, NA, 4L, 5L)
+  testthat::expect_true(is.na(caretEnsemble::wtd.sd(x_na, w)))
+  testthat::expect_false(is.na(caretEnsemble::wtd.sd(x_na, w, na.rm = TRUE)))
+
+  testthat::expect_error(caretEnsemble::wtd.sd(x, w[-1L]), "'x' and 'w' must have the same length")
+
+  x3 <- c(10L, 10L, 10L, 20L)
+  w1 <- c(0.1, 0.1, 0.1, 0.7)
+  testthat::expect_equal(caretEnsemble::wtd.sd(x3, w = w1), 5.291503, tolerance = 0.001)
+  testthat::expect_equal(caretEnsemble::wtd.sd(x3, w = w1 * 100L), caretEnsemble::wtd.sd(x3, w = w1), tolerance = 0.001)
+})
+
+######################################################################
+testthat::context("set_excluded_class_id")
+######################################################################
+
+testthat::test_that("set_excluded_class_id warning if unset", {
+  old_ensemble <- ens.class
+  old_ensemble$excluded_class_id <- NULL
+
+  is_class <- isClassifier(old_ensemble)
+  new_ensemble <- expect_warning(
+    set_excluded_class_id(old_ensemble, is_class),
+    "No excluded_class_id set. Setting to 1L."
+  )
+
+  testthat::expect_identical(new_ensemble$excluded_class_id, 1L)
 })
diff --git a/tests/testthat/test-classSelection.R b/tests/testthat/test-classSelection.R
index 62c4e7fa..af631e2a 100644
--- a/tests/testthat/test-classSelection.R
+++ b/tests/testthat/test-classSelection.R
@@ -1,28 +1,21 @@
-testthat::context("Does binary class selection work?")
-
 # Load and prepare data for subsequent tests
 seed <- 2239L
 set.seed(seed)
-data(models.class)
-data(X.class)
-data(Y.class)
+utils::data(iris)
 
 # Create 80/20 train/test split
-index <- caret::createDataPartition(Y.class, p = 0.8)[[1L]]
-X.train <- X.class[index, ]
-X.test <- X.class[-index, ]
-Y.train <- Y.class[index]
-Y.test <- Y.class[-index]
-
-#############################################################################
-testthat::context("Do classifier predictions use the correct target classes?")
-#############################################################################
+target_col <- which(names(iris) == "Species")
+index <- caret::createDataPartition(iris[, target_col], p = 0.8)[[1L]]
+X.train <- iris[index, -target_col]
+X.test <- iris[-index, -target_col]
+Y.train <- iris[index, target_col]
+Y.test <- iris[-index, target_col]
 
 runBinaryLevelValidation <- function(Y.train, Y.test, pos.level = 1L) {
   # Extract levels of response input data
   Y.levels <- levels(Y.train)
   testthat::expect_identical(Y.levels, levels(Y.test))
-  testthat::expect_length(Y.levels, 2L)
+  testthat::expect_length(Y.levels, 3L)
 
   # Train a caret ensemble
   model.list <- caretList(
@@ -73,50 +66,31 @@ runBinaryLevelValidation <- function(Y.train, Y.test, pos.level = 1L) {
   # check exists to avoid previous errors where classifer ensemble predictions were
   # being made using the incorrect level of the response, causing the opposite
   # class labels to be predicted with new data.
-  testthat::expect_gt(cmat.pred$overall["Accuracy"], 0.79)
+  testthat::expect_gt(cmat.pred$overall["Accuracy"], 0.60)
 
   # Similar to the above, ensure that probability predictions are working correctly
   # by checking to see that accuracy is also high for class predictions created
   # from probabilities
-  testthat::expect_gt(cmat.cutoff$overall["Accuracy"], 0.79)
+  testthat::expect_gt(cmat.cutoff$overall["Accuracy"], 0.60)
 }
 
-testthat::test_that("Ensembled classifiers do not rearrange outcome factor levels", {
-  # First run the level selection test using the default levels
-  # of the response (i.e. c('No', 'Yes'))
-  set.seed(seed)
-  runBinaryLevelValidation(Y.train, Y.test, pos.level = 1L)
+#############################################################################
+testthat::context("Do classifier predictions use the correct target classes?")
+#############################################################################
 
-  # Now reverse the assigment of the response labels as well as
-  # the levels of the response factor. Reversing the assignment
-  # is necessary to make sure the expected accuracy numbers are
-  # the same (i.e. Making a "No" into a "Yes" in the response means
-  # predictions of the first class will still be as accurate).
-  # Reversing the level order then ensures that the outcome is not
-  # releveled at some point by caretEnsemble.
-  Y.levels <- levels(Y.train)
-  refactor <- function(d) {
-    factor(
-      ifelse(d == Y.levels[1L], Y.levels[2L], Y.levels[1L]),
-      levels = rev(Y.levels)
-    )
+testthat::test_that("validateExcludedClass for multiclass", {
+  for (excluded_class in c(1L, 2L, 3L)) {
+    testthat::expect_silent(validateExcludedClass(excluded_class))
   }
-
-  set.seed(seed)
-  runBinaryLevelValidation(refactor(Y.train), refactor(Y.test))
+  testthat::expect_error(validateExcludedClass("x"), "classification excluded level must be numeric")
 })
 
 testthat::test_that("Target class selection configuration works", {
-  # No error
-  excluded_class <- validateExcludedClass(1L)
-  excluded_class <- validateExcludedClass(2L)
-
-  # Should error
-  testthat::expect_error(validateExcludedClass("x"), "classification excluded level must be numeric")
-
-  # Check that we can exclude the first class
   Y.levels <- levels(Y.train)
   refactor <- function(d) factor(as.character(d), levels = rev(Y.levels))
   set.seed(seed)
-  runBinaryLevelValidation(refactor(Y.train), refactor(Y.test), pos.level = 1L)
+  for (pos in c(1L, 2L, 3L)) {
+    runBinaryLevelValidation(Y.train, Y.test, pos.level = pos)
+    runBinaryLevelValidation(refactor(Y.train), refactor(Y.test), pos.level = pos)
+  }
 })
diff --git a/tests/testthat/test-ensembleMethods.R b/tests/testthat/test-ensembleMethods.R
deleted file mode 100644
index cac9430d..00000000
--- a/tests/testthat/test-ensembleMethods.R
+++ /dev/null
@@ -1,115 +0,0 @@
-# Are tests failing here?
-# UPDATE THE FIXTURES!
-# make update-test-fixtures
-
-testthat::context("Does variable importance work?")
-
-data(models.reg)
-data(X.reg)
-data(Y.reg)
-
-data(models.class)
-data(X.class)
-data(Y.class)
-
-ens.class <- caretEnsemble(
-  models.class,
-  metric = "ROC",
-  trControl = caret::trainControl(
-    number = 2L,
-    summaryFunction = caret::twoClassSummary,
-    classProbs = TRUE,
-    savePredictions = TRUE
-  )
-)
-ens.reg <- caretEnsemble(models.reg, trControl = caret::trainControl(number = 2L, savePredictions = TRUE))
-
-testthat::test_that("caret::varImp.caretEnsemble", {
-  set.seed(2239L)
-
-  for (m in list(ens.class, ens.reg)) {
-    for (s in c(TRUE, FALSE)) {
-      i <- caret::varImp(m, normalize = s)
-      testthat::expect_is(i, "numeric")
-      if (isClassifier(m)) {
-        len <- length(m$models) * 2L
-        n <- c(outer(c("rf", "glm", "rpart", "treebag"), c("No", "Yes"), paste, sep = "_"))
-        n <- matrix(n, ncol = 2L)
-        n <- c(t(n))
-      } else {
-        len <- length(m$models)
-        n <- names(m$models)
-      }
-      testthat::expect_length(i, len)
-      testthat::expect_named(i, n)
-      if (s) {
-        testthat::expect_true(all(i >= 0.0))
-        testthat::expect_true(all(i <= 1.0))
-        testthat::expect_equal(sum(i), 1.0, tolerance = 1e-6)
-      }
-    }
-  }
-})
-
-testthat::test_that("plot.caretEnsemble", {
-  for (ens in list(ens.class, ens.reg)) {
-    plt <- plot(ens)
-    testthat::expect_is(plt, "ggplot")
-    testthat::expect_identical(nrow(plt$data), 5L) # 4 models, one ensemble
-    testthat::expect_named(ens$models, plt$data$model_name[-1L]) # First is ensemble
-  }
-})
-
-testthat::test_that("ggplot2::autoplot.caretEnsemble", {
-  for (ens in list(ens.class, ens.reg)) {
-    plt1 <- ggplot2::autoplot(ens)
-    plt2 <- ggplot2::autoplot(ens, xvars = c("Petal.Length", "Petal.Width"))
-
-    testthat::expect_is(plt1, "ggplot")
-    testthat::expect_is(plt2, "ggplot")
-
-    testthat::expect_is(plt1, "patchwork")
-    testthat::expect_is(plt2, "patchwork")
-
-    train_model <- ens.reg$models[[1L]]
-    testthat::expect_error(ggplot2::autoplot(train_model), "Objects of class (.*?) are not supported by autoplot")
-  }
-})
-
-testthat::test_that("summary.caretEnsemble", {
-  for (ens in list(ens.class, ens.reg)) {
-    smry <- testthat::expect_silent(summary(ens.class))
-    testthat::expect_output(print(smry), ens.class$ens_model$metric)
-    for (name in names(ens.class$models)) {
-      testthat::expect_output(print(smry), name)
-    }
-  }
-})
-
-testthat::test_that("extractModelMetrics", {
-  for (ens in list(ens.class, ens.reg)) {
-    metrics <- extractMetric(ens)
-    testthat::expect_s3_class(metrics, "data.table")
-    testthat::expect_named(ens$models, metrics$model_name[-1L])
-  }
-})
-
-testthat::test_that("precict.caretEnsemble with and without se and weights", {
-  for (ens in list(ens.class, ens.reg)) {
-    is_class <- isClassifier(ens)
-    for (se in c(FALSE, TRUE)) {
-      p <- predict(
-        ens,
-        newdata = X.reg,
-        se = se,
-        excluded_class_id = 1L
-      )
-      expect_s3_class(p, "data.table")
-      if (se) {
-        testthat::expect_named(p, c("pred", "lwr", "upr"))
-      } else {
-        testthat::expect_named(p, ifelse(is_class, "Yes", "pred"))
-      }
-    }
-  }
-})
diff --git a/tests/testthat/test-helper_functions.R b/tests/testthat/test-helper_functions.R
deleted file mode 100644
index 894253fb..00000000
--- a/tests/testthat/test-helper_functions.R
+++ /dev/null
@@ -1,444 +0,0 @@
-########################################################################
-testthat::context("Do the helper functions work for regression objects?")
-########################################################################
-
-data(models.reg)
-data(X.reg)
-data(Y.reg)
-
-data(models.class)
-data(X.class)
-data(Y.class)
-
-testthat::test_that("Recycling generates a warning", {
-  testthat::expect_error(
-    caretEnsemble::wtd.sd(matrix(1L:10L, ncol = 2L), w = 1L),
-    "'x' and 'w' must have the same length"
-  )
-})
-
-testthat::test_that("No predictions generates an error", {
-  models_multi <- caretList(
-    iris[, 1L:2L], iris[, 5L],
-    tuneLength = 1L, verbose = FALSE,
-    methodList = c("rf", "gbm")
-  )
-  testthat::expect_is(vapply(models_multi, isClassifierAndValidate, logical(1L)), "logical")
-
-  models <- caretList(
-    iris[, 1L:2L], factor(ifelse(iris[, 5L] == "setosa", "Yes", "No")),
-    tuneLength = 1L, verbose = FALSE,
-    methodList = c("rf", "gbm")
-  )
-  new_model <- caret::train(
-    iris[, 1L:2L], factor(ifelse(iris[, 5L] == "setosa", "Yes", "No")),
-    tuneLength = 1L,
-    method = "glmnet",
-    metric = "ROC",
-    trControl = caret::trainControl(
-      method = "cv",
-      number = 2L,
-      classProbs = TRUE,
-      summaryFunction = caret::twoClassSummary,
-      savePredictions = "final"
-    )
-  )
-  models2 <- c(new_model, models)
-  models3 <- c(models, new_model)
-  testthat::expect_is(vapply(models, isClassifierAndValidate, logical(1L)), "logical")
-  testthat::expect_is(vapply(models2, isClassifierAndValidate, logical(1L)), "logical")
-  testthat::expect_is(vapply(models3, isClassifierAndValidate, logical(1L)), "logical")
-})
-
-testthat::test_that("We can make the stacked predictions matrix", {
-  out <- predict(models.reg)
-  testthat::expect_s3_class(out, "data.table")
-  testthat::expect_identical(dim(out), c(150L, 4L))
-  testthat::expect_named(out, c("rf", "glm", "rpart", "treebag"))
-})
-
-testthat::test_that("We can predict", {
-  out <- predict(models.reg, newdata = X.reg)
-  testthat::expect_is(out, "data.table")
-  testthat::expect_identical(dim(out), c(150L, 4L))
-  testthat::expect_named(out, c("rf", "glm", "rpart", "treebag"))
-})
-
-########################################################################
-testthat::context("Do the helper functions work for classification objects?")
-########################################################################
-
-testthat::test_that("We can make the stacked predictions matrix", {
-  out <- predict(models.class)
-  testthat::expect_s3_class(out, "data.table")
-  testthat::expect_identical(dim(out), c(150L, 4L * 1L)) # number of models * (number of classes-1)
-})
-
-testthat::test_that("We can predict", {
-  out <- predict(models.class, newdata = X.class, excluded_class_id = 0L)
-  testthat::expect_is(out, "data.table")
-  testthat::expect_identical(dim(out), c(150L, 4L * 2L))
-  model_names <- c("rf", "glm", "rpart", "treebag")
-  class_names <- c("No", "Yes")
-  combinations <- expand.grid(class_names, model_names)
-  testthat::expect_named(out, paste(combinations$Var2, combinations$Var1, sep = "_"))
-  out2 <- predict(models.reg, newdata = X.reg)
-  testthat::expect_identical(dim(out2), c(150L, 4L))
-  testthat::expect_named(out2, c("rf", "glm", "rpart", "treebag"))
-})
-
-testthat::test_that("predict results same regardless of verbose option", {
-  invisible(capture.output({
-    testthat::expect_is(predict(models.class, newdata = X.class), "data.table")
-    out1 <- predict(models.class, newdata = X.class)
-    out2 <- predict(models.class, verbose = TRUE, newdata = X.class)
-    testthat::expect_identical(out1, out2)
-
-    testthat::expect_is(predict(models.reg, newdata = X.reg), "data.table")
-    out1 <- predict(models.reg, newdata = X.reg)
-    out2 <- predict(models.reg, verbose = TRUE, newdata = X.reg)
-    testthat::expect_identical(out1, out2)
-  }))
-})
-
-testthat::context("Test weighted standard deviations")
-
-testthat::test_that("wtd.sd applies weights correctly", {
-  x1 <- c(3L, 5L, 9L, 3L, 4L, 6L, 4L)
-  x2 <- c(10L, 10L, 20L, 14L, 2L, 2L, 40L)
-  x3 <- c(10L, 10L, 10L, 20L)
-  w1 <- c(0.1, 0.1, 0.1, 0.7)
-  testthat::expect_error(caretEnsemble::wtd.sd(x1), 'argument "w" is missing, with no default')
-  testthat::expect_false(sd(x1) == caretEnsemble::wtd.sd(x1, w = x2))
-  testthat::expect_false(sd(x1) == caretEnsemble::wtd.sd(x1, w = x2))
-  testthat::expect_equal(caretEnsemble::wtd.sd(x3, w = w1), 5.291503, tolerance = 0.001)
-  testthat::expect_equal(caretEnsemble::wtd.sd(x3, w = w1 * 100L), caretEnsemble::wtd.sd(x3, w = w1), tolerance = 0.001)
-})
-
-testthat::test_that("wtd.sd handles NA values correctly", {
-  x1 <- c(10L, 10L, 10L, 20L, NA, NA)
-  w1 <- c(0.1, 0.1, 0.1, 0.7, NA, NA)
-  testthat::expect_true(is.na(caretEnsemble::wtd.sd(x1, w = w1)))
-  testthat::expect_true(is.na(sd(x1)))
-  testthat::expect_false(is.na(caretEnsemble::wtd.sd(x1, w = w1, na.rm = TRUE)))
-  testthat::expect_false(is.na(sd(x1, na.rm = TRUE)))
-  testthat::expect_true(is.na(caretEnsemble::wtd.sd(x1, w = w1)))
-  testthat::expect_false(is.na(caretEnsemble::wtd.sd(x1, w = w1, na.rm = TRUE)))
-})
-
-testthat::test_that("caretList supports combined regression, binary, multiclass", {
-  set.seed(42L)
-
-  # Regression models
-  reg_models <- caretList(
-    Sepal.Length ~ Sepal.Width,
-    iris,
-    methodList = c("glm", "lm")
-  )
-  testthat::expect_is(predict(reg_models), "data.table")
-
-  # Binary model
-  bin_models <- caretList(
-    factor(ifelse(Species == "setosa", "Yes", "No")) ~ Sepal.Width,
-    iris,
-    methodList = c("lda", "rpart")
-  )
-  testthat::expect_is(predict(bin_models), "data.table")
-
-  # Multiclass model
-  multi_models <- caretList(
-    Species ~ Sepal.Width,
-    iris,
-    methodList = "rpart"
-  )
-  testthat::expect_is(predict(multi_models), "data.table")
-
-  # Combine them!
-  all_models <- c(reg_models, bin_models, multi_models)
-  testthat::expect_s3_class(all_models, "caretList")
-  testthat::expect_is(vapply(all_models, isClassifierAndValidate, logical(1L)), "logical")
-
-  # Test preds
-  stacked_p <- predict(all_models)
-  new_p <- predict(all_models, newdata = iris[seq_len(10L), ])
-  testthat::expect_is(stacked_p, "data.table")
-  testthat::expect_is(new_p, "data.table")
-  testthat::expect_identical(nrow(stacked_p), nrow(iris))
-  testthat::expect_identical(nrow(new_p), 10L)
-})
-
-testthat::test_that("isClassifierAndValidate shouldn't care about predictions", {
-  model_list <- models.class
-  model_list[[1L]]$pred <- NULL
-  testthat::expect_is(vapply(model_list, isClassifierAndValidate, logical(1L)), "logical")
-  testthat::expect_equivalent(unique(vapply(model_list, isClassifierAndValidate, logical(1L))), TRUE)
-})
-
-testthat::test_that("isClassifierAndValidate stops when a classification model can't predict probabilities", {
-  model_list <- models.class
-  model_list[[1L]]$modelInfo$prob <- FALSE
-  err <- "No probability function found. Re-fit with a method that supports prob."
-  testthat::expect_error(lapply(model_list, isClassifierAndValidate), err)
-})
-
-testthat::test_that("isClassifierAndValidate stops when a classification model did not save probs", {
-  model_list <- models.class
-  model_list[[1L]]$control$classProbs <- FALSE
-  err <- "classProbs = FALSE. Re-fit with classProbs = TRUE in trainControl."
-  testthat::expect_error(lapply(model_list, isClassifierAndValidate, validate_for_stacking = TRUE), err)
-  testthat::context("Test helper functions for multiclass classification")
-
-  testthat::test_that("Configuration function for excluded level work", {
-    # Integers work
-    testthat::expect_identical(validateExcludedClass(0L), 0L)
-    testthat::expect_identical(validateExcludedClass(1L), 1L)
-    testthat::expect_identical(validateExcludedClass(4L), 4L)
-
-    # Decimals work with a warning
-    wrn <- "classification excluded level is not an integer:"
-    testthat::expect_warning(testthat::expect_identical(validateExcludedClass(0.0), 0L), wrn)
-    testthat::expect_warning(testthat::expect_identical(validateExcludedClass(1.0), 1L), wrn)
-    testthat::expect_warning(testthat::expect_identical(validateExcludedClass(4.0), 4L), wrn)
-
-    # Less than 0 will error
-    testthat::expect_error(validateExcludedClass(-1L), "classification excluded level must be >= 0: -1")
-
-    # Make a model list
-    data(iris)
-    model_list <- caretList(
-      x = iris[, -5L],
-      y = iris[, 5L],
-      methodList = c("rpart", "glmnet")
-    )
-
-    # Stacking with the excluded level should work
-    invisible(caretStack(model_list, method = "knn", excluded_class_id = 1L))
-
-    # Stacking with too great of a level should work. No error or warning.
-    # Should also validate it?
-    stack <- caretStack(model_list, method = "knn", excluded_class_id = 4L)
-    invisible(predict(stack, iris[, -5L]))
-
-    # Check if we are actually excluding level 1 (setosa)
-    classes <- levels(iris[, 5L])[-1L]
-    models <- c("rpart", "glmnet")
-    class_model_combinations <- expand.grid(classes, models)
-    varImp_rownames <- apply(class_model_combinations, 1L, function(x) paste(x[2L], x[1L], sep = "_"))
-
-    model_stack <- caretStack(model_list, method = "knn", excluded_class_id = 1L)
-    testthat::expect_identical(rownames(caret::varImp(model_stack$ens_model)$importance), varImp_rownames)
-  })
-})
-
-# Tests for validateExcludedClass function
-testthat::test_that("validateExcludedClass stops for non-numeric input", {
-  invalid_input <- "invalid"
-  err <- "classification excluded level must be numeric: invalid"
-  testthat::expect_error(validateExcludedClass(invalid_input), err)
-})
-
-testthat::test_that("validateExcludedClass stops for non-finite input", {
-  invalid_input <- Inf
-  err <- "classification excluded level must be finite: Inf"
-  testthat::expect_warning(
-    testthat::expect_error(validateExcludedClass(invalid_input), err),
-    "classification excluded level is not an integer: Inf"
-  )
-})
-
-testthat::test_that("validateExcludedClass stops for non-positive input", {
-  invalid_input <- -1.0
-  err <- "classification excluded level must be >= 0: -1"
-  wrn <- "classification excluded level is not an integer:"
-  testthat::expect_warning(testthat::expect_error(validateExcludedClass(invalid_input), err), wrn)
-})
-
-validated <- testthat::test_that("validateExcludedClass warns for non-integer input", {
-  testthat::expect_identical(
-    testthat::expect_warning(
-      validateExcludedClass(1.1),
-      "classification excluded level is not an integer: 1.1"
-    ), 1L
-  )
-})
-
-testthat::test_that("validateExcludedClass passes for valid input", {
-  valid_input <- 3L
-  testthat::expect_identical(validateExcludedClass(valid_input), 3L)
-})
-
-########################################################################
-testthat::context("Helper function edge cases")
-########################################################################
-
-testthat::test_that("wtd.sd calculates weighted standard deviation correctly", {
-  x <- c(1L, 2L, 3L, 4L, 5L)
-  w <- c(1L, 1L, 1L, 1L, 1L)
-  testthat::expect_equal(wtd.sd(x, w), sd(x), tol = 0.001)
-
-  w <- c(2L, 1L, 1L, 1L, 1L)
-  testthat::expect_true(wtd.sd(x, w) != sd(x))
-
-  # Test with NA values
-  x_na <- c(1L, 2L, NA, 4L, 5L)
-  testthat::expect_true(is.na(wtd.sd(x_na, w)))
-  testthat::expect_false(is.na(wtd.sd(x_na, w, na.rm = TRUE)))
-
-  # Test error for mismatched lengths
-  testthat::expect_error(wtd.sd(x, w[-1L]), "'x' and 'w' must have the same length")
-})
-
-testthat::test_that("isClassifierAndValidate validates caretList correctly", {
-  testthat::expect_is(vapply(models.class, isClassifierAndValidate, logical(1L)), "logical")
-  testthat::expect_is(vapply(models.reg, isClassifierAndValidate, logical(1L)), "logical")
-
-  # Test error for non-caretList object
-  testthat::expect_error(
-    isClassifierAndValidate(list(model = lm(Y.reg ~ ., data = as.data.frame(X.reg)))),
-    "is(object, \"train\") is not TRUE",
-    fixed = TRUE
-  )
-})
-
-testthat::test_that("isClassifierAndValidate validates model types correctly", {
-  testthat::expect_is(vapply(models.class, isClassifierAndValidate, logical(1L)), "logical")
-  testthat::expect_is(vapply(models.reg, isClassifierAndValidate, logical(1L)), "logical")
-
-  # Test error for mixed model types
-  mixed_list <- c(models.class, models.reg)
-  testthat::expect_is(vapply(mixed_list, isClassifierAndValidate, logical(1L)), "logical")
-})
-
-testthat::test_that("Stacked predictions for caret lists works", {
-  best_preds_class <- predict(models.class)
-  best_preds_reg <- predict(models.reg)
-
-  testthat::expect_is(best_preds_class, "data.table")
-  testthat::expect_is(best_preds_reg, "data.table")
-
-  testthat::expect_named(best_preds_class, names(models.class))
-  testthat::expect_named(best_preds_reg, names(models.reg))
-})
-
-testthat::test_that("Stacked predictions works with different resampling strategies", {
-  models.class.inconsistent <- models.class
-  models.class.inconsistent[[1L]]$pred$Resample <- "WEIRD_SAMPLING"
-  testthat::expect_is(predict(models.class.inconsistent), "data.table")
-})
-
-testthat::test_that("Stacked predictions works if the row indexes differ", {
-  models.class.inconsistent <- models.class
-  models.class.inconsistent[[1L]]$pred$rowIndex <- rev(models.class.inconsistent[[1L]]$pred$rowIndex)
-  big_preds <- rbind(models.class.inconsistent[[2L]]$pred, models.class.inconsistent[[2L]]$pred)
-  models.class.inconsistent[[2L]]$pred <- big_preds
-  testthat::expect_is(predict(models.class.inconsistent), "data.table")
-})
-
-testthat::test_that("extractModelName extracts model names correctly", {
-  testthat::expect_identical(extractModelName(models.class[[1L]]), "rf")
-  testthat::expect_identical(extractModelName(models.reg[[1L]]), "rf")
-
-  # Test custom model
-  custom_model <- models.class[[1L]]
-  custom_model$method <- list(method = "custom_rf")
-  testthat::expect_identical(extractModelName(custom_model), "custom_rf")
-})
-
-testthat::test_that("isClassifierAndValidate extracts model types correctly", {
-  testthat::expect_true(unique(vapply(models.class, isClassifierAndValidate, logical(1L))))
-  testthat::expect_false(unique(vapply(models.reg, isClassifierAndValidate, logical(1L))))
-})
-
-testthat::test_that("caretPredict extracts best predictions correctly", {
-  stacked_preds_class <- caretPredict(models.class[[1L]], excluded_class_id = 0L)
-  stacked_preds_reg <- caretPredict(models.reg[[1L]])
-
-  testthat::expect_s3_class(stacked_preds_class, "data.table")
-  testthat::expect_s3_class(stacked_preds_reg, "data.table")
-
-  testthat::expect_named(stacked_preds_class, c("No", "Yes"))
-  testthat::expect_named(stacked_preds_reg, "pred")
-})
-
-testthat::test_that("Stacked predictions creates prediction-observation data correctly", {
-  stacked_preds_class <- predict(models.class)
-  stacked_preds_reg <- predict(models.reg)
-
-  testthat::expect_s3_class(stacked_preds_class, "data.table")
-  testthat::expect_s3_class(stacked_preds_reg, "data.table")
-
-  testthat::expect_identical(ncol(stacked_preds_class), length(models.class))
-  testthat::expect_identical(ncol(stacked_preds_reg), length(models.reg))
-
-  testthat::expect_named(stacked_preds_class, names(models.class))
-  testthat::expect_named(stacked_preds_reg, names(stacked_preds_reg))
-
-  testthat::expect_identical(nrow(stacked_preds_class), 150L)
-  testthat::expect_identical(nrow(stacked_preds_reg), 150L)
-})
-
-testthat::test_that("Stacked predictions works on new model types", {
-  # Note that new model types would have to return a single column called 'pred'
-  models.class.new <- models.reg
-  for (idx in seq_along(models.class.new)) {
-    models.class.new[[idx]]$modelType <- "TimeSeries"
-  }
-  preds <- predict(models.class.new)
-  testthat::expect_s3_class(preds, "data.table")
-})
-
-testthat::test_that("validateExcludedClass validates excluded level correctly", {
-  testthat::expect_warning(validateExcludedClass(NULL), "No excluded_class_id set. Setting to 1L.")
-  testthat::expect_error(
-    validateExcludedClass(c(1L, 2L)),
-    "classification excluded level must have a length of 1: length=2"
-  )
-  testthat::expect_error(validateExcludedClass("a"), "classification excluded level must be numeric: a")
-  testthat::expect_error(validateExcludedClass(-1L), "classification excluded level must be >= 0: -1")
-  testthat::expect_warning(
-    testthat::expect_error(validateExcludedClass(-0.000001), "classification excluded level must be >= 0: -1e-06"),
-    "classification excluded level is not an integer"
-  )
-  testthat::expect_warning(
-    testthat::expect_error(validateExcludedClass(Inf), "classification excluded level must be finite: Inf"),
-    "classification excluded level is not an integer"
-  )
-  testthat::expect_warning(validateExcludedClass(1.5), "classification excluded level is not an integer: 1.5")
-  txt <- "classification excluded level is not an integer: 2"
-  testthat::expect_warning(
-    testthat::expect_identical(
-      validateExcludedClass(2.0), 2L
-    ), txt,
-    "classification excluded level is not an integer"
-  )
-})
-
-testthat::test_that("validateExcludedClass validates excluded level correctly", {
-  testthat::expect_warning(validateExcludedClass(NULL), "No excluded_class_id set. Setting to 1L.")
-  testthat::expect_error(
-    validateExcludedClass(c(1L, 2L)),
-    "classification excluded level must have a length of 1: length=2"
-  )
-  testthat::expect_error(validateExcludedClass("a"), "classification excluded level must be numeric: a")
-  testthat::expect_error(validateExcludedClass(-1L), "classification excluded level must be >= 0: -1")
-  testthat::expect_warning(
-    testthat::expect_error(validateExcludedClass(-0.000001), "classification excluded level must be >= 0: -1e-06"),
-    "classification excluded level is not an integer"
-  )
-  testthat::expect_warning(
-    testthat::expect_error(validateExcludedClass(Inf), "classification excluded level must be finite: Inf"),
-    "classification excluded level is not an integer"
-  )
-  testthat::expect_warning(validateExcludedClass(1.5), "classification excluded level is not an integer: 1.5")
-  txt <- "classification excluded level is not an integer: 2"
-  testthat::expect_warning(testthat::expect_identical(validateExcludedClass(2.0), 2L), txt)
-})
-
-testthat::test_that("isClassifierAndValidate fails for models without object$control$savePredictions", {
-  model <- models.class[[1L]]
-  model$control$savePredictions <- NULL
-  err <- "Must have savePredictions = 'all', 'final', or TRUE in trainControl to do stacked predictions."
-  testthat::expect_error(isClassifierAndValidate(model), err)
-  model$control$savePredictions <- "BAD_VALUE"
-  testthat::expect_error(isClassifierAndValidate(model), err)
-})
diff --git a/tests/testthat/test-multiclass.R b/tests/testthat/test-multiclass.R
index 8a825633..54d4a5f0 100644
--- a/tests/testthat/test-multiclass.R
+++ b/tests/testthat/test-multiclass.R
@@ -1,16 +1,23 @@
-#############################################################################
-testthat::context("caretList and caretStack work for multiclass problems")
-#############################################################################
-testthat::test_that("We can predict with caretList and caretStack multiclass problems", {
-  data(iris)
-  model_list <- caretList(
-    x = iris[, -5L],
-    y = iris[, 5L],
-    methodList = c("glmnet", "rpart")
+utils::data(iris)
+utils::data(Boston, package = "MASS")
+
+model_list <- caretList(
+  iris[, -5L],
+  iris[, 5L],
+  tuneLength = 1L,
+  methodList = c("glmnet", "rpart"),
+  tuneList = list(
+    nnet = caretModelSpec(method = "nnet", trace = FALSE)
   )
+)
 
+######################################################################
+testthat::context("caretList and caretStack work for multiclass problems")
+######################################################################
+
+testthat::test_that("We can predict with caretList and caretStack for multiclass problems", {
   p <- predict(model_list, newdata = iris[, -5L])
-  testthat::expect_is(p, "data.table")
+  testthat::expect_s3_class(p, "data.table")
   testthat::expect_identical(nrow(p), nrow(iris))
 
   ens <- caretStack(model_list, method = "rpart")
@@ -25,20 +32,9 @@ testthat::test_that("We can predict with caretList and caretStack multiclass pro
 })
 
 testthat::test_that("Columns for caretList predictions are correct and ordered", {
-  data(iris)
-  model_list <- caretList(
-    x = iris[, -5L],
-    y = iris[, 5L],
-    methodList = c("glmnet", "rpart"),
-    tuneList = list(
-      nnet = caretModelSpec(method = "nnet", trace = FALSE)
-    )
-  )
-
   num_methods <- length(model_list)
   num_classes <- length(unique(iris$Species))
 
-  # Check the number of rows and columns is correct
   p <- predict(model_list, newdata = iris[, -5L], excluded_class_id = 0L)
   testthat::expect_identical(dim(p), c(nrow(iris), num_methods * num_classes))
 
@@ -47,48 +43,32 @@ testthat::test_that("Columns for caretList predictions are correct and ordered",
   class_method_combinations <- expand.grid(classes, methods)
   ordered_colnames <- apply(class_method_combinations, 1L, function(x) paste(x[2L], x[1L], sep = "_"))
 
-  # Check the names of the columns are correct
   testthat::expect_true(all(colnames(p) %in% ordered_colnames))
-
-  # Check that the columns are ordered correctly
   testthat::expect_named(p, ordered_colnames)
 })
 
 testthat::test_that("Columns for caretStack are correct", {
-  data(iris)
-  model_list <- caretList(
-    x = iris[, -5L],
-    y = iris[, 5L],
-    methodList = "rpart",
-    tuneList = list(
-      nnet = caretModelSpec(method = "nnet", trace = FALSE)
-    )
-  )
-
   model_stack <- caretStack(model_list, method = "knn")
 
   num_classes <- length(unique(iris$Species))
+  classes <- levels(iris$Species)
 
-  # Check the number of rows and columns is correct
   p_raw <- predict(model_stack, newdata = iris[, -5L])
   testthat::expect_identical(nrow(p_raw), nrow(iris))
+
   p_prob <- predict(model_stack, newdata = iris[, -5L])
   testthat::expect_identical(dim(p_prob), c(nrow(iris), num_classes))
-
-  classes <- levels(iris$Species)
-
-  # Check that the columns are ordered correctly
   testthat::expect_named(p_prob, classes)
 })
 
 testthat::test_that("Periods are supported in method and class names in caretList and caretStack", {
-  data(iris)
-  # Rename values and levels to have underscores
-  levels(iris[, 5L]) <- c("setosa_1", "versicolor_2", "virginica_3")
-  iris[, 5L] <- factor(iris[, 5L])
+  iris_mod <- iris
+  levels(iris_mod[, 5L]) <- c("setosa_1", "versicolor_2", "virginica_3")
+  iris_mod[, 5L] <- factor(iris_mod[, 5L])
+
   model_list <- caretList(
-    x = iris[, -5L],
-    y = iris[, 5L],
+    x = iris_mod[, -5L],
+    y = iris_mod[, 5L],
     methodList = c("glmnet", "rpart"),
     tuneList = list(
       nnet_1 = caretModelSpec(
@@ -105,10 +85,9 @@ testthat::test_that("Periods are supported in method and class names in caretLis
   )
 
   methods <- names(model_list)
-  classes <- levels(iris[, 5L])
-
-  p <- predict(model_list, newdata = iris[, -5L], excluded_class_id = 0L)
+  classes <- levels(iris_mod[, 5L])
 
+  p <- predict(model_list, newdata = iris_mod[, -5L], excluded_class_id = 0L)
   class_method_combinations <- expand.grid(classes, methods)
   ordered_colnames <- apply(class_method_combinations, 1L, function(x) paste(x[2L], x[1L], sep = "_"))
   testthat::expect_named(p, ordered_colnames)
@@ -116,58 +95,32 @@ testthat::test_that("Periods are supported in method and class names in caretLis
   model_stack <- caretStack(model_list, method = "knn", trControl = trainControl(
     savePredictions = "final", classProbs = TRUE
   ))
-  p_prob <- predict(model_stack, newdata = iris[, -5L])
+  p_prob <- predict(model_stack, newdata = iris_mod[, -5L])
   testthat::expect_named(p_prob, classes)
-  p_raw <- predict(model_stack, newdata = iris[, -5L])
+  p_raw <- predict(model_stack, newdata = iris_mod[, -5L])
   testthat::expect_named(p_raw, classes)
 })
 
 testthat::test_that("We can make a confusion matrix", {
-  data(iris)
-
-  set.seed(42L)
-  n <- nrow(iris)
-  train_indices <- sample.int(n, n * 0.8)
-  train_data <- iris[train_indices, ]
-  test_data <- iris[-train_indices, ]
-  model_list <- caretList(
-    x = train_data[, -5L],
-    y = train_data[, 5L],
-    methodList = c("glmnet", "rpart"),
-    tuneList = list(
-      nnet = caretModelSpec(method = "nnet", trace = FALSE)
-    )
-  )
-
   model_stack <- caretStack(model_list, method = "knn")
 
-  # Make a confusion matrix
-  predictions <- predict(model_stack, newdata = test_data[, -5L])
-  classes <- apply(predictions, 1L, function(x) names(x)[which.max(x)])
-  classes <- factor(classes, levels = levels(test_data[, 5L]))
-  cm <- confusionMatrix(classes, test_data[, 5L])
-  testthat::expect_is(cm, "confusionMatrix")
+  predictions <- predict(model_stack, newdata = iris[, -5L], return_class_only = TRUE)
+  cm <- caret::confusionMatrix(predictions, iris[, 5L])
 
-  # Check dims
+  testthat::expect_s3_class(cm, "confusionMatrix")
   testthat::expect_identical(dim(cm$table), c(3L, 3L))
-  # Accuracy should be greater than 0.9
-  testthat::expect_gt(cm$overall["Accuracy"], 0.9)
+  testthat::expect_gt(cm$overall["Accuracy"], 0.95) # In sample accuracy should be high
 })
 
 testthat::test_that("caretList and caretStack handle imbalanced multiclass data", {
   set.seed(123L)
   n <- 1000L
-  X <- data.table::data.table(x1 = rnorm(n), x2 = rnorm(n))
+  X <- data.table::data.table(x1 = stats::rnorm(n), x2 = stats::rnorm(n))
   y <- factor(c(rep("A", 700L), rep("B", 200L), rep("C", 100L)))
 
-  model_list <- caretList(
-    x = X,
-    y = y,
-    methodList = c("rpart", "glmnet")
-  )
-
+  model_list <- caretList(X, y, methodList = "rpart")
   testthat::expect_s3_class(model_list, "caretList")
-  testthat::expect_length(model_list, 2L)
+  testthat::expect_length(model_list, 1L)
 
   stack <- caretStack(model_list, method = "glmnet")
   testthat::expect_s3_class(stack, "caretStack")
@@ -179,15 +132,10 @@ testthat::test_that("caretList and caretStack handle imbalanced multiclass data"
 testthat::test_that("caretList and caretStack handle a large number of classes", {
   set.seed(123L)
   n <- 1000L
-  X <- data.table::data.table(x1 = rnorm(n), x2 = rnorm(n))
+  X <- data.table::data.table(x1 = stats::rnorm(n), x2 = stats::rnorm(n))
   y <- factor(sample(paste0("Class", 1L:100L), n, replace = TRUE))
 
-  model_list <- caretList(
-    x = X,
-    y = y,
-    methodList = "rpart"
-  )
-
+  model_list <- caretList(X, y, methodList = "rpart")
   testthat::expect_s3_class(model_list, "caretList")
 
   stack <- caretStack(model_list, method = "rpart")
@@ -198,16 +146,10 @@ testthat::test_that("caretList and caretStack handle a large number of classes",
 })
 
 testthat::test_that("caretList and caretStack handle ordinal multiclass data", {
-  data(Boston, package = "MASS")
   Boston$chas <- as.factor(Boston$chas)
   Boston$rad <- factor(paste0("rad_", Boston$rad), ordered = TRUE)
 
-  model_list <- caretList(
-    rad ~ .,
-    data = Boston,
-    methodList = c("rpart", "glmnet")
-  )
-
+  model_list <- caretList(rad ~ ., data = Boston, methodList = c("rpart", "glmnet"))
   testthat::expect_s3_class(model_list, "caretList")
 
   stack <- caretStack(model_list, method = "rpart")
@@ -216,18 +158,10 @@ testthat::test_that("caretList and caretStack handle ordinal multiclass data", {
   preds <- predict(stack, newdata = Boston)
   testthat::expect_s3_class(preds, "data.table")
   testthat::expect_named(preds, levels(Boston$rad))
-  testthat::expect_equal(rowSums(preds), rep(1.0, nrow(Boston)), tol = 0.0001)
+  testthat::expect_equal(rowSums(preds), rep(1.0, nrow(Boston)), tolerance = 1e-4)
 })
 
 testthat::test_that("caretList and caretStack produce consistent probability predictions", {
-  data(iris)
-
-  model_list <- caretList(
-    x = iris[, -5L],
-    y = iris[, 5L],
-    methodList = c("rpart", "glmnet")
-  )
-
   stack <- caretStack(model_list, method = "rpart")
 
   prob_preds <- predict(stack, newdata = iris[, -5L])
@@ -238,7 +172,7 @@ testthat::test_that("caretList and caretStack produce consistent probability pre
 })
 
 testthat::test_that("caretList and caretStack handle new levels in prediction data", {
-  data(iris)
+  set.seed(123L)
   idx <- seq_len(nrow(iris))
   idx_train <- sample(idx, 120L)
   idx_test <- setdiff(idx, idx_train)
@@ -247,32 +181,9 @@ testthat::test_that("caretList and caretStack handle new levels in prediction da
   test_data$Species <- factor(as.character(test_data$Species), levels = c(levels(iris$Species), "NewSpecies"))
   test_data$Species[1L] <- "NewSpecies"
 
-  model_list <- caretList(
-    x = train_data[, -5L],
-    y = train_data[, 5L],
-    methodList = c("rf", "rpart")
-  )
-
+  model_list <- caretList(train_data[, -5L], train_data[, 5L], methodList = c("rf", "rpart"))
   stack <- caretStack(model_list, method = "rpart")
 
   preds <- predict(stack, newdata = test_data)
-  testthat::expect_true(all(levels(preds) %in% levels(train_data$Species)))
-})
-
-testthat::test_that("caretList and caretStack produce consistent probability predictions", {
-  data(iris)
-
-  model_list <- caretList(
-    x = iris[, -5L],
-    y = iris[, 5L],
-    methodList = c("rpart", "glmnet")
-  )
-
-  stack <- caretStack(model_list, method = "rpart")
-
-  prob_preds <- predict(stack, newdata = iris[, -5L])
-  testthat::expect_identical(nrow(prob_preds), nrow(iris))
-  testthat::expect_identical(ncol(prob_preds), nlevels(iris$Species))
-  testthat::expect_true(all(rowSums(prob_preds) >= 0.99))
-  testthat::expect_true(all(rowSums(prob_preds) <= 1.01))
+  testthat::expect_true(all(colnames(preds) %in% levels(train_data$Species)))
 })
diff --git a/tests/testthat/test-permutationImportance.R b/tests/testthat/test-permutationImportance.R
index d6211c23..2aa772c4 100644
--- a/tests/testthat/test-permutationImportance.R
+++ b/tests/testthat/test-permutationImportance.R
@@ -1,21 +1,20 @@
-data(models.class)
-data(models.reg)
-data(iris)
+# Helper functions
+utils::data(models.class)
+utils::data(models.reg)
+utils::data(iris)
 
-# Helper function to create a simple dataset
 create_dataset <- function(n = 200L, p = 5L, classification = TRUE) {
   set.seed(42L)
-  X <- data.table::data.table(matrix(rnorm(n * p), ncol = p))
+  X <- data.table::data.table(matrix(stats::rnorm(n * p), ncol = p))
   data.table::setnames(X, paste0("x", seq_len(p)))
   if (classification) {
     y <- factor(ifelse(rowSums(X) > 0L, "A", "B"))
   } else {
-    y <- rowSums(X) + rnorm(n)
+    y <- rowSums(X) + stats::rnorm(n)
   }
   list(X = X, y = y)
 }
 
-# Helper function to train a model
 train_model <- function(x, y, method = "rpart", ...) {
   set.seed(1234L)
   caret::train(
@@ -27,7 +26,6 @@ train_model <- function(x, y, method = "rpart", ...) {
   )
 }
 
-# Helper function to check test results
 check_importance_scores <- function(
     imp,
     expected_names = paste0("x", seq_len(5L)),
@@ -40,24 +38,37 @@ check_importance_scores <- function(
   testthat::expect_equal(sum(imp), 1L, tolerance = 1e-6)
 }
 
-testthat::test_that("isClassifier works for train models", {
+######################################################################
+testthat::context("isClassifier function")
+######################################################################
+
+testthat::test_that("isClassifier works for train models and caretStacks models", {
   testthat::expect_true(isClassifier(models.class[[1L]]))
   testthat::expect_false(isClassifier(models.reg[[1L]]))
-})
 
-testthat::test_that("isClassifier works for caretStacks models", {
-  ens_class <- caretEnsemble(models.class)
-  ens_reg <- caretEnsemble(models.reg)
+  ens_class <- caretEnsemble::caretEnsemble(models.class)
+  ens_reg <- caretEnsemble::caretEnsemble(models.reg)
 
   testthat::expect_true(isClassifier(ens_class))
   testthat::expect_false(isClassifier(ens_reg))
 })
 
-testthat::test_that("permutationImportance works for regression", {
-  dt <- create_dataset(classification = FALSE)
-  model <- train_model(dt[["X"]], dt[["y"]])
-  imp <- permutationImportance(model, dt[["X"]])
-  check_importance_scores(imp)
+######################################################################
+testthat::context("permutationImportance function")
+######################################################################
+
+testthat::test_that("permutationImportance works for regression and classification", {
+  # Regression
+  dt_reg <- create_dataset(classification = FALSE)
+  model_reg <- train_model(dt_reg[["X"]], dt_reg[["y"]])
+  imp_reg <- permutationImportance(model_reg, dt_reg[["X"]])
+  check_importance_scores(imp_reg)
+
+  # Classification
+  dt_class <- create_dataset(classification = TRUE)
+  model_class <- train_model(dt_class[["X"]], dt_class[["y"]])
+  imp_class <- permutationImportance(model_class, dt_class[["X"]])
+  check_importance_scores(imp_class)
 })
 
 testthat::test_that("permutationImportance works for multiclass classification", {
@@ -69,13 +80,13 @@ testthat::test_that("permutationImportance works for multiclass classification",
     x3 = stats::rnorm(n)
   )
   coef_matrix <- matrix(c(
-    1.0, -0.5, 0.2, # coefficients for class A
-    -0.5, 1.0, 0.2, # coefficients for class B
-    0.2, 0.2, 1.0 # coefficients for class C
+    1.0, -0.5, 0.2,
+    -0.5, 1.0, 0.2,
+    0.2, 0.2, 1.0
   ), nrow = 3L, byrow = TRUE)
 
   linear_combinations <- as.matrix(x) %*% t(coef_matrix)
-  linear_combinations <- linear_combinations + matrix(stats::rnorm(n * 3.0, sd = 0.1), nrow = n)
+  linear_combinations <- linear_combinations + matrix(stats::rnorm(n * 3L, sd = 0.1), nrow = n)
   probabilities <- exp(linear_combinations) / rowSums(exp(linear_combinations))
   y <- factor(apply(probabilities, 1L, function(prob) sample(c("A", "B", "C"), 1L, prob = prob)))
 
@@ -84,174 +95,120 @@ testthat::test_that("permutationImportance works for multiclass classification",
   check_importance_scores(imp, c("x1", "x2", "x3"))
 })
 
-testthat::test_that("permutationImportance works with a single feature unimportant feature", {
+testthat::test_that("permutationImportance works with single feature cases", {
   n <- 100L
-  x <- data.table::data.table(x1 = stats::rnorm(n))
-  y <- factor(sample(c("A", "B"), n, replace = TRUE))
-  model <- train_model(x, y)
-  imp <- permutationImportance(model, x)
-  check_importance_scores(imp, "x1")
-})
-
-testthat::test_that("permutationImportance works with a single important feature", {
-  set.seed(1234L)
-
-  make_var <- function(n) scale(stats::rnorm(n), center = TRUE, scale = TRUE)[, 1L]
-
-  n <- 1000L
-  x <- data.table::data.table(
-    x1 = make_var(n),
-    x2 = make_var(n),
-    x3 = make_var(n)
-  )
-
-  cf_set <- c(0L, 1L, 5L, 10L)
-  all_cfs <- expand.grid(
-    c(0L, 1L),
-    cf_set,
-    cf_set,
-    cf_set
-  )
-
-  evaluate_model <- function(cf, do_class) {
-    cf <- unname(unlist(cf))
-    y <- (cbind(1L, as.matrix(x)) %*% cf)[, 1L]
-    if (do_class) {
-      classes <- c("A", "B")
-      y <- factor(ifelse(y > 0L, classes[1L], classes[2L]), levels = classes)
-      if (length(unique(y)) == 1L) {
-        return(NULL)
-      }
-    }
-    model <- suppressWarnings(train_model(x, y, method = "glm"))
-    imp <- permutationImportance(model, x)
-    check_importance_scores(imp, c("x1", "x2", "x3"))
-
-    glm_imp <- normalize_to_one(abs(coef(model$finalModel))[-1L])
-    cf_norm <- normalize_to_one(cf[-1L])
-    testthat::expect_equivalent(glm_imp, cf_norm, tolerance = 0.1)
 
-    if (!do_class || cf[[1L]] == 0.0) {
-      testthat::expect_equivalent(imp, cf_norm, tolerance = 0.1)
-    }
-  }
-
-  for (do_class in c(FALSE, TRUE)) {
-    apply(all_cfs, 1L, evaluate_model, do_class)
-  }
+  # Unimportant feature
+  x_unimp <- data.table::data.table(x1 = stats::rnorm(n))
+  y_unimp <- factor(sample(c("A", "B"), n, replace = TRUE))
+  model_unimp <- train_model(x_unimp, y_unimp)
+  imp_unimp <- permutationImportance(model_unimp, x_unimp)
+  check_importance_scores(imp_unimp, "x1")
+
+  # Important feature
+  x_imp <- data.table::data.table(x1 = stats::rnorm(n))
+  y_imp <- x_imp$x1 + stats::rnorm(n, sd = 0.1)
+  model_imp <- train_model(x_imp, y_imp, method = "lm")
+  imp_imp <- permutationImportance(model_imp, x_imp)
+  check_importance_scores(imp_imp, "x1")
+  testthat::expect_gt(imp_imp["x1"], 0.9)
 })
 
-testthat::test_that("permutationImportance works a single, contant, unimportant feature", {
+testthat::test_that("permutationImportance works with constant features", {
   n <- 100L
-  x <- data.table::data.table(
-    x1 = rep(1L, n),
-    x2 = stats::rnorm(n)
-  )
-  y <- stats::rnorm(n)
-  model <- train_model(x, y)
-  imp <- permutationImportance(model, x)
-  check_importance_scores(imp, c("x1", "x2"))
-  testthat::expect_lte(imp["x1"], imp["x2"])
-})
 
-testthat::test_that("permutationImportance works a single, contant, important feature - aka intercept only", {
-  n <- 100L
-  x <- data.table::data.table(
-    x1 = rep(1L, n),
-    x2 = stats::rnorm(n)
-  )
-  y <- x$x1 + stats::rnorm(n) / 10L
-  model <- train_model(x, y)
-  imp <- permutationImportance(model, x)
-  check_importance_scores(imp, c("x1", "x2"))
-  testthat::expect_lte(imp["x1"], imp["x2"])
+  # Constant unimportant feature
+  x_const_unimp <- data.table::data.table(x1 = rep(1L, n), x2 = stats::rnorm(n))
+  y_const_unimp <- stats::rnorm(n)
+  model_const_unimp <- train_model(x_const_unimp, y_const_unimp)
+  imp_const_unimp <- permutationImportance(model_const_unimp, x_const_unimp)
+  check_importance_scores(imp_const_unimp, c("x1", "x2"))
+  testthat::expect_lte(imp_const_unimp["x1"], imp_const_unimp["x2"])
+
+  # Constant important feature (intercept only)
+  x_const_imp <- data.table::data.table(x1 = rep(1L, n), x2 = stats::rnorm(n))
+  y_const_imp <- x_const_imp$x1 + stats::rnorm(n, sd = 0.1)
+  model_const_imp <- train_model(x_const_imp, y_const_imp)
+  imp_const_imp <- permutationImportance(model_const_imp, x_const_imp)
+  check_importance_scores(imp_const_imp, c("x1", "x2"))
+  testthat::expect_lte(imp_const_imp["x2"], imp_const_imp["x1"])
 })
 
 testthat::test_that("permutationImportance works with perfect predictor", {
   n <- 100L
-  x <- data.table::data.table(
-    x1 = stats::rnorm(n),
-    x2 = stats::rnorm(n)
-  )
+  x <- data.table::data.table(x1 = stats::rnorm(n), x2 = stats::rnorm(n))
   y <- x$x1
   model <- train_model(x, y, method = "lm")
   imp <- permutationImportance(model, x)
   check_importance_scores(imp, c("x1", "x2"))
-  testthat::expect_gt(imp["x1"], imp["x2"])
+  testthat::expect_gt(imp["x1"], 0.9)
+  testthat::expect_lt(imp["x2"], 0.1)
 })
 
-testthat::test_that("permutationImportance works for multiclass classification and various edge cases", {
+testthat::test_that("permutationImportance works for multiclass classification with iris dataset", {
   model <- train_model(iris[, -5L], iris$Species, method = "rpart")
   imp <- permutationImportance(model, iris[, -5L])
   check_importance_scores(imp, names(iris[, -5L]))
 })
 
+######################################################################
 testthat::context("permutationImportance edge cases")
-testthat::test_that("permutationImportance normalizes to uniform distribution for all zero importances", {
-  n <- 100L
-  x <- data.table::data.table(x1 = rep(0L, n), x2 = rep(0L, n), x3 = rep(0L, n))
-  y <- rep(0L, n)
-  model <- train_model(x, y, method = "lm")
-  imp <- permutationImportance(model, x)
-  check_importance_scores(imp, names(x))
-  testthat::expect_equivalent(imp, normalize_to_one(rep(0L, length(imp))), tolerance = 1e-6)
-})
+######################################################################
 
-testthat::test_that("permutationImportance assigns full importance to perfect predictor", {
-  set.seed(1234L)
+testthat::test_that("permutationImportance handles various edge cases", {
   n <- 100L
   vars <- 25L
-  x <- data.table::data.table(
-    matrix(rnorm(n * vars), nrow = n, ncol = vars)
-  )
-  data.table::setnames(x, paste0("x", seq_len(vars)))
-  y <- x$x1
-  model <- train_model(x, y, method = "lm")
-  imp <- permutationImportance(model, x)
-  check_importance_scores(imp, names(x))
-  testthat::expect_equal(imp[["x1"]], 1L, tol = 1e-8)
-  testthat::expect_equal(sum(imp[-1L]), 0L, tol = 1e-8)
-})
 
-testthat::test_that("permutationImportance handles highly collinear features", {
-  set.seed(5678L)
-  n <- 100L
-  x <- data.table::data.table(
-    x1 = rnorm(n),
-    x2 = rnorm(n)
+  # All zero importances
+  x_zero <- data.table::data.table(matrix(0L, nrow = n, ncol = 3L))
+  y_zero <- rep(0L, n)
+  model_zero <- train_model(x_zero, y_zero, method = "lm")
+  imp_zero <- permutationImportance(model_zero, x_zero)
+  check_importance_scores(imp_zero, names(x_zero))
+  testthat::expect_equivalent(imp_zero, normalize_to_one(rep(0L, length(imp_zero))), tolerance = 1e-6)
+
+  # Perfect predictor among many variables
+  x_perfect <- data.table::data.table(matrix(stats::rnorm(n * vars), nrow = n, ncol = vars))
+  data.table::setnames(x_perfect, paste0("x", seq_len(vars)))
+  y_perfect <- x_perfect$x1
+  model_perfect <- train_model(x_perfect, y_perfect, method = "lm")
+  imp_perfect <- permutationImportance(model_perfect, x_perfect)
+  check_importance_scores(imp_perfect, names(x_perfect))
+  testthat::expect_equal(imp_perfect[["x1"]], 1L, tol = 1e-8)
+  testthat::expect_equal(sum(imp_perfect[-1L]), 0L, tol = 1e-8)
+
+  # Highly collinear features
+  x_collinear <- data.table::data.table(
+    x1 = stats::rnorm(n),
+    x2 = stats::rnorm(n)
   )
-  x$x3 <- x$x1 + rnorm(n, sd = 0.01)
-  y <- x$x1 + x$x2
-  model <- train_model(x, y, method = "lm")
-  imp <- permutationImportance(model, x)
-  check_importance_scores(imp, names(x))
-  testthat::expect_equal(imp[["x3"]], 0L, tol = 1e-6)
-})
-
-testthat::test_that("permutationImportance works with very small dataset", {
-  set.seed(9876L)
-  n <- 5L
-  x <- data.table::data.table(
-    x1 = rnorm(n),
-    x2 = rnorm(n),
-    x3 = rnorm(n)
+  x_collinear$x3 <- x_collinear$x1 + stats::rnorm(n, sd = 0.01)
+  y_collinear <- x_collinear$x1 + x_collinear$x2
+  model_collinear <- train_model(x_collinear, y_collinear, method = "lm")
+  imp_collinear <- permutationImportance(model_collinear, x_collinear)
+  check_importance_scores(imp_collinear, names(x_collinear))
+  testthat::expect_lt(imp_collinear[["x3"]], 0.1)
+
+  # Very small dataset
+  x_small <- data.table::data.table(
+    x1 = stats::rnorm(5L),
+    x2 = stats::rnorm(5L),
+    x3 = stats::rnorm(5L)
   )
-  y <- x$x1 + rnorm(n)
-  model <- train_model(x, y, method = "lm")
-  imp <- permutationImportance(model, x)
-  check_importance_scores(imp, names(x))
-})
+  y_small <- x_small$x1 + stats::rnorm(5L)
+  model_small <- train_model(x_small, y_small, method = "lm")
+  imp_small <- permutationImportance(model_small, x_small)
+  check_importance_scores(imp_small, names(x_small))
 
-testthat::test_that("permutationImportance handles identical features", {
-  n <- 100L
-  x <- data.table::data.table(
-    x1 = rnorm(n),
-    x2 = rnorm(n)
+  # Identical features
+  x_identical <- data.table::data.table(
+    x1 = stats::rnorm(n),
+    x2 = stats::rnorm(n)
   )
-  x$x3 <- x$x1
-  y <- x$x1 + x$x2 + rnorm(n)
-  model <- train_model(x, y, method = "glmnet")
-  imp <- permutationImportance(model, x)
-  check_importance_scores(imp, names(x))
-  testthat::expect_equal(imp[["x1"]], imp[["x3"]], tol = 1e-1)
+  x_identical$x3 <- x_identical$x1
+  y_identical <- x_identical$x1 + x_identical$x2 + stats::rnorm(n)
+  model_identical <- train_model(x_identical, y_identical, method = "glmnet")
+  imp_identical <- permutationImportance(model_identical, x_identical)
+  check_importance_scores(imp_identical, names(x_identical))
+  testthat::expect_equal(imp_identical[["x1"]], imp_identical[["x3"]], tol = 1e-1)
 })