kable-didnt-work

README.md

@@ -46,10 +46,10 @@ print(summary(models))
 #> The following models were ensembled: rf, glmnet  
 #> 
 #> Model accuracy:
-#>    model_name metric    value       sd
-#>        <char> <char>    <num>    <num>
-#> 1:         rf   RMSE 1065.605 145.4863
-#> 2:     glmnet   RMSE 1206.800 175.7538
+#>    model_name metric     value       sd
+#>        <char> <char>     <num>    <num>
+#> 1:         rf   RMSE  960.6879 185.7793
+#> 2:     glmnet   RMSE 1129.2088 141.2402
 ```
 
 Then, use caretEnsemble to make a greedy ensemble of these models
@@ -67,18 +67,18 @@ print(greedy_stack)
 #> Summary of sample sizes: 400, 400, 400, 400, 400 
 #> Resampling results:
 #> 
-#>   RMSE      Rsquared  MAE     
-#>   1043.649  0.92586   543.4189
+#>   RMSE      Rsquared   MAE    
+#>   935.4086  0.9478161  527.013
 #> 
 #> Tuning parameter 'max_iter' was held constant at a value of 100
 #> 
 #> Final model:
 #> Greedy MSE
-#> RMSE:  1044.709 
+#> RMSE:  933.8605 
 #> Weights:
 #>        [,1]
-#> rf     0.72
-#> glmnet 0.28
+#> rf      0.7
+#> glmnet  0.3
 ggplot2::autoplot(greedy_stack, training_data = dat, xvars = c("carat", "table"))
 ```
 
@@ -100,8 +100,8 @@ print(rf_stack)
 #> Summary of sample sizes: 400, 400, 400, 400, 400 
 #> Resampling results:
 #> 
-#>   RMSE     Rsquared   MAE     
-#>   1049.47  0.9219408  522.2029
+#>   RMSE      Rsquared   MAE     
+#>   849.1342  0.9573736  456.2256
 #> 
 #> Tuning parameter 'mtry' was held constant at a value of 2
 #> 
@@ -113,8 +113,8 @@ print(rf_stack)
 #>                      Number of trees: 500
 #> No. of variables tried at each split: 2
 #> 
-#>           Mean of squared residuals: 1188152
-#>                     % Var explained: 91.49
+#>           Mean of squared residuals: 669531.3
+#>                     % Var explained: 95.8
 ggplot2::autoplot(rf_stack, training_data = dat, xvars = c("carat", "table"))
 ```
 

README.rmd

@@ -11,12 +11,6 @@ knitr::opts_chunk$set(
   fig.path = "man/figures/README-",
   out.width = "100%"
 )
-knitr::opts_chunk$set(render = function(x, options) {
-  if (is.data.frame(x) || is.matrix(x)) {
-    return(knitr::kable(x))
-  }
-  knitr::knit_print(x)
-})
 ```
 
 # caretEnsemble

vignettes/Version-4.0-New-Features.Rmd

@@ -17,12 +17,6 @@ knitr::opts_chunk$set(
   warning = FALSE,
   message = FALSE
 )
-knitr::opts_chunk$set(render = function(x, options) {
-  if (is.data.frame(x) || is.matrix(x)) {
-    return(knitr::kable(x))
-  }
-  knitr::knit_print(x)
-})
 set.seed(42L)
 ```
 

vignettes/caretEnsemble-intro.Rmd

@@ -17,12 +17,6 @@ knitr::opts_chunk$set(
   warning = FALSE,
   message = FALSE
 )
-knitr::opts_chunk$set(render = function(x, options) {
-  if (is.data.frame(x) || is.matrix(x)) {
-    return(knitr::kable(x))
-  }
-  knitr::knit_print(x)
-})
 ```
 
 `caretEnsemble` is a package for making ensembles of caret models. You should already be somewhat familiar with the caret package before trying out `caretEnsemble`.
@@ -34,22 +28,23 @@ knitr::opts_chunk$set(render = function(x, options) {
 `caretList` is a flexible function for fitting many different caret models, with the same resampling parameters, to the same dataset. It returns a convenient `list` of caret objects which can later be passed to `caretEnsemble` and `caretStack`. `caretList` has almost exactly the same arguments as `train` (from the caret package), with the exception that the `trControl` argument comes last. It can handle both the formula interface and the explicit `x`, `y` interface to train. As in caret, the formula interface introduces some overhead and the `x`, `y` interface is preferred.
 
 `caretEnsemble` has 2 arguments that can be used to specify which models to fit: `methodList` and `tuneList`. `methodList` is a simple character vector of methods that will be fit with the default `train` parameters, while `tuneList` can be used to customize the call to each component model and will be discussed in more detail later. First, lets build an example dataset (adapted from the caret vignette):
-```{r, results="hide"}
+```{r}
 data(Sonar, package = "mlbench")
 set.seed(107L)
 inTrain <- caret::createDataPartition(y = Sonar$Class, p = 0.75, list = FALSE)
 training <- Sonar[inTrain, ]
 testing <- Sonar[-inTrain, ]
 ```
 
-```{r, echo=TRUE, results="hide", warning=FALSE}
+```{r}
 model_list <- caretEnsemble::caretList(
   Class ~ .,
   data = training,
   methodList = c("glmnet", "rpart")
 )
 print(summary(model_list))
 ```
+
 (As with `train`, the formula interface is convenient but introduces move overhead. For large datasets the explicitly passing `x` and `y` is preferred).
 We can use the `predict` function to extract predictions from this object for new data:
 ```{r}