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Version 4.0 New Features

Zach Deane-Mayer

2024-09-12

Multiclass support

caretEnsemble now fully supports multiclass problems:

model_list <- caretEnsemble::caretList(
  x = iris[, 1L:4L],
  y = iris[, 5L],
  methodList = c("rpart", "rf")
)
print(summary(model_list))
#> The following models were ensembled: rpart, rf  
#> 
#> Model accuracy:
#>    model_name   metric     value         sd
#>        <char>   <char>     <num>      <num>
#> 1:      rpart Accuracy 0.9333333 0.04082483
#> 2:         rf Accuracy 0.9600000 0.02788867

Greedy Optimizer in caretEnsemble

The new version uses a greedy optimizer by default, ensuring the ensemble is never worse than the worst single model:

ens <- caretEnsemble::caretEnsemble(model_list)
print(summary(ens))
#> The following models were ensembled: rpart, rf  
#> 
#> Model Importance:
#>     rpart_setosa rpart_versicolor  rpart_virginica        rf_setosa 
#>           0.2937           0.0101           0.0122           0.0000 
#>    rf_versicolor     rf_virginica 
#>           0.3303           0.3537 
#> 
#> Model accuracy:
#>    model_name   metric     value         sd
#>        <char>   <char>     <num>      <num>
#> 1:   ensemble Accuracy 0.9600000 0.01490712
#> 2:      rpart Accuracy 0.9333333 0.04082483
#> 3:         rf Accuracy 0.9600000 0.02788867

Enhanced S3 Methods

caretStack (and by extension, caretEnsemble) now supports various S3 methods:

print(ens)
#> The following models were ensembled: rpart, rf  
#> 
#> caret::train model:
#> Greedy Mean Squared Error Optimizer 
#> 
#> No pre-processing
#> Resampling: Cross-Validated (5 fold) 
#> Summary of sample sizes: 120, 120, 120, 120, 120 
#> Resampling results:
#> 
#>   Accuracy  Kappa
#>   0.96      0.94 
#> 
#> Tuning parameter 'max_iter' was held constant at a value of 100
#> 
#> Final model:
#> Greedy MSE
#> RMSE:  0.1476488 
#> Weights:
#>                  setosa versicolor virginica
#> rpart_setosa          1       0.00      0.00
#> rpart_versicolor      0       0.00      0.03
#> rpart_virginica       0       0.03      0.00
#> rf_setosa             0       0.00      0.00
#> rf_versicolor         0       0.97      0.00
#> rf_virginica          0       0.00      0.97
print(summary(ens))
#> The following models were ensembled: rpart, rf  
#> 
#> Model Importance:
#>     rpart_setosa rpart_versicolor  rpart_virginica        rf_setosa 
#>           0.2579           0.0113           0.0113           0.0000 
#>    rf_versicolor     rf_virginica 
#>           0.3490           0.3705 
#> 
#> Model accuracy:
#>    model_name   metric     value         sd
#>        <char>   <char>     <num>      <num>
#> 1:   ensemble Accuracy 0.9600000 0.01490712
#> 2:      rpart Accuracy 0.9333333 0.04082483
#> 3:         rf Accuracy 0.9600000 0.02788867

plot(ens)

A dot and whisker plot of ROC for glmnet, rpart, and an ensemble. The ensemble has the highest ROC and is slighly better than the glmnet. The rpart model is bad.

ggplot2::autoplot(ens)

A 4-panel plot for glmnet, rpart, and an ensemble. The ensemble has the highest ROC and is slighly better than the glmnet. The rpart model is bad. The glmnet has the highest weight, and the residuals look biased.

Improved Default trainControl

A new default trainControl constructor makes it easier to build appropriate controls for caretLists. These controls include explicit indexes based on the target, return stacked predictions, and use probability estimates for classification models.

class_control <- caretEnsemble::defaultControl(iris$Species)
print(ls(class_control))
#>  [1] "adaptive"          "allowParallel"     "classProbs"       
#>  [4] "fixedWindow"       "horizon"           "index"            
#>  [7] "indexFinal"        "indexOut"          "initialWindow"    
#> [10] "method"            "number"            "p"                
#> [13] "preProcOptions"    "predictionBounds"  "repeats"          
#> [16] "returnData"        "returnResamp"      "sampling"         
#> [19] "savePredictions"   "search"            "seeds"            
#> [22] "selectionFunction" "skip"              "summaryFunction"  
#> [25] "timingSamps"       "trim"              "verboseIter"

reg_control <- caretEnsemble::defaultControl(iris$Sepal.Length)
print(ls(reg_control))
#>  [1] "adaptive"          "allowParallel"     "classProbs"       
#>  [4] "fixedWindow"       "horizon"           "index"            
#>  [7] "indexFinal"        "indexOut"          "initialWindow"    
#> [10] "method"            "number"            "p"                
#> [13] "preProcOptions"    "predictionBounds"  "repeats"          
#> [16] "returnData"        "returnResamp"      "sampling"         
#> [19] "savePredictions"   "search"            "seeds"            
#> [22] "selectionFunction" "skip"              "summaryFunction"  
#> [25] "timingSamps"       "trim"              "verboseIter"

Mixed Resampling Strategies

Models with different resampling strategies can now be ensembled:

y <- iris[, 1L]
x <- iris[, 2L:3L]
flex_list <- caretEnsemble::caretList(
  x = x,
  y = y,
  methodList = c("rpart", "rf"),
  trControl = caretEnsemble::defaultControl(y, number = 3L)
)
#> note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .

flex_list$glm_boot <- caret::train(
  x = x,
  y = y,
  method = "glm",
  trControl = caretEnsemble::defaultControl(y, method = "boot", number = 25L)
)

flex_ens <- caretEnsemble::caretEnsemble(flex_list)
print(flex_ens)
#> The following models were ensembled: rpart, rf, glm_boot  
#> 
#> caret::train model:
#> Greedy Mean Squared Error Optimizer 
#> 
#> No pre-processing
#> Resampling: Cross-Validated (5 fold) 
#> Summary of sample sizes: 120, 121, 120, 119, 120 
#> Resampling results:
#> 
#>   RMSE       Rsquared   MAE      
#>   0.3213821  0.8504616  0.2652383
#> 
#> Tuning parameter 'max_iter' was held constant at a value of 100
#> 
#> Final model:
#> Greedy MSE
#> RMSE:  0.3204963 
#> Weights:
#>          [,1]
#> rpart    0.00
#> rf       0.51
#> glm_boot 0.49

Mixed Model Types

caretEnsemble now allows ensembling of mixed lists of classification and regression models:

X <- iris[, 1L:4L]

target_class <- iris[, 5L]
target_reg <- as.integer(iris[, 5L] == "virginica")

ctrl_class <- caretEnsemble::defaultControl(target_class)
ctrl_reg <- caretEnsemble::defaultControl(target_reg)

model_class <- caret::train(iris[, 1L:4L], target_class, method = "rf", trControl = ctrl_class)
model_reg <- caret::train(iris[, 1L:4L], target_reg, method = "rf", trControl = ctrl_reg)
mixed_list <- caretEnsemble::as.caretList(list(class = model_class, reg = model_reg))
mixed_ens <- caretEnsemble::caretEnsemble(mixed_list)
print(mixed_ens)
#> The following models were ensembled: class, reg  
#> 
#> caret::train model:
#> Greedy Mean Squared Error Optimizer 
#> 
#> No pre-processing
#> Resampling: Cross-Validated (5 fold) 
#> Summary of sample sizes: 120, 120, 120, 120, 120 
#> Resampling results:
#> 
#>   Accuracy   Kappa
#>   0.9466667  0.92 
#> 
#> Tuning parameter 'max_iter' was held constant at a value of 100
#> 
#> Final model:
#> Greedy MSE
#> RMSE:  0.1520195 
#> Weights:
#>                  setosa versicolor virginica
#> class_setosa          1       0.01      0.00
#> class_versicolor      0       0.97      0.01
#> class_virginica       0       0.02      0.00
#> reg                   0       0.00      0.99

Transfer Learning

caretStack now supports transfer learning for ensembling models trained on different datasets:

train_idx <- sample.int(nrow(iris), 100L)
train_data <- iris[train_idx, ]
new_data <- iris[-train_idx, ]

model_list <- caretEnsemble::caretList(
  x = train_data[, 1L:4L],
  y = train_data[, 5L],
  methodList = c("rpart", "rf")
)

transfer_ens <- caretEnsemble::caretEnsemble(
  model_list,
  new_X = new_data[, 1L:4L],
  new_y = new_data[, 5L]
)

print(transfer_ens)
#> The following models were ensembled: rpart, rf  
#> 
#> caret::train model:
#> Greedy Mean Squared Error Optimizer 
#> 
#> No pre-processing
#> Resampling: Cross-Validated (5 fold) 
#> Summary of sample sizes: 39, 41, 40, 40, 40 
#> Resampling results:
#> 
#>   Accuracy  Kappa    
#>   0.96      0.9398462
#> 
#> Tuning parameter 'max_iter' was held constant at a value of 100
#> 
#> Final model:
#> Greedy MSE
#> RMSE:  0.1090264 
#> Weights:
#>                  setosa versicolor virginica
#> rpart_setosa          1       0.00         0
#> rpart_versicolor      0       0.00         0
#> rpart_virginica       0       0.00         0
#> rf_setosa             0       0.01         0
#> rf_versicolor         0       0.99         0
#> rf_virginica          0       0.00         1

We can also predict on new data:

preds <- predict(transfer_ens, newdata = head(new_data))
knitr::kable(preds, format = "markdown")

setosa	versicolor	virginica
0.9900990	0.0099010	0
0.9900990	0.0099010	0
0.9900990	0.0099010	0
0.9881814	0.0118186	0
0.9900990	0.0099010	0
0.9900990	0.0099010	0

Permutation Importance

Permutation importance is now the default method for variable importance in caretLists and caretStacks:

importance <- caret::varImp(transfer_ens)
print(round(importance, 2L))
#>     rpart_setosa rpart_versicolor  rpart_virginica        rf_setosa 
#>             0.36             0.00             0.00             0.00 
#>    rf_versicolor     rf_virginica 
#>             0.32             0.31

Note that the ensemble uses rpart to classify the easy class (setosa) and then uses the rf to distinguish between the 2 more difficult classes.

This completes our demonstration of the key new features in caretEnsemble 4.0. These enhancements provide greater flexibility, improved performance, and easier usage for ensemble modeling in R.

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They may not be fully stable and should be used with caution. We make no claims about them.
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