kable

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 1007.595 192.05082
-#> 2:     glmnet   RMSE 1217.374  69.54145
+#>    model_name metric    value       sd
+#>        <char> <char>    <num>    <num>
+#> 1:         rf   RMSE 1065.605 145.4863
+#> 2:     glmnet   RMSE 1206.800 175.7538
 ```
 
 Then, use caretEnsemble to make a greedy ensemble of these models
@@ -68,17 +68,17 @@ print(greedy_stack)
 #> Resampling results:
 #> 
 #>   RMSE      Rsquared  MAE     
-#>   951.4063  0.947765  545.1398
+#>   1043.649  0.92586   543.4189
 #> 
 #> Tuning parameter 'max_iter' was held constant at a value of 100
 #> 
 #> Final model:
 #> Greedy MSE
-#> RMSE:  959.2989 
+#> RMSE:  1044.709 
 #> Weights:
 #>        [,1]
-#> rf     0.68
-#> glmnet 0.32
+#> rf     0.72
+#> glmnet 0.28
 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     
-#>   1030.494  0.9376481  541.5582
+#>   RMSE     Rsquared   MAE     
+#>   1049.47  0.9219408  522.2029
 #> 
 #> 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: 985743.6
-#>                     % Var explained: 93.99
+#>           Mean of squared residuals: 1188152
+#>                     % Var explained: 91.49
 ggplot2::autoplot(rf_stack, training_data = dat, xvars = c("carat", "table"))
 ```
 

README.rmd

@@ -11,6 +11,12 @@ 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

@@ -17,6 +17,12 @@ 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

@@ -17,6 +17,12 @@ 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`.