Skip to contents

Combine several predictive models via weights

Source: R/caretEnsemble.R, R/package.R
caretEnsemble.Rd

Find a greedy, positive only linear combination of several train objects

Functions for creating ensembles of caret models: caretList and caretStack

Usage

caretEnsemble(all.models, excluded_class_id = 0L, tuneLength = 1L, ...)

Arguments

all.models

an object of class caretList

excluded_class_id

The integer level to exclude from binary classification or multiclass problems. By default no classes are excluded, as the greedy optimizer requires all classes because it cannot use negative coefficients.

tuneLength

The size of the grid to search for tuning the model. Defaults to 1, as the only parameter to optimize is the number of iterations, and the default of 100 works well.

...

additional arguments to pass caret::train

Value

a caretEnsemble object

Details

greedyMSE works well when you want an ensemble that will never be worse than any single model in the dataset. In the worst case scenario, it will select the single best model, if none of them can be ensembled to improve the overall score. It will also never assign any model a negative coefficient, which can help avoid unintuitive cases at prediction time (e.g. if the correlations between predictors breaks down on new data, negative coefficients can lead to bad results).

Note

Every model in the "library" must be a separate train object. For example, if you wish to combine a random forests with several different values of mtry, you must build a model for each value of mtry. If you use several values of mtry in one train model, (e.g. tuneGrid = expand.grid(.mtry=2:5)), caret will select the best value of mtry before we get a chance to include it in the ensemble. By default, RMSE is used to ensemble regression models, and AUC is used to ensemble Classification models. This function does not currently support multi-class problems

See also

Useful links:

Author

Maintainer: Zachary A. Deane-Mayer zach.mayer@gmail.com [copyright holder]

Other contributors:

Examples

set.seed(42)
models <- caretList(iris[1:50, 1:2], iris[1:50, 3], methodList = c("rpart", "rf"))
#> Warning: There were missing values in resampled performance measures.
#> note: only 1 unique complexity parameters in default grid. Truncating the grid to 1 .
#> 
ens <- caretEnsemble(models)
summary(ens)
#> The following models were ensembled: rpart, rf  
#> 
#> Model Importance:
#> rpart    rf 
#> 0.952 0.048 
#> 
#> Model accuracy:
#>    model_name metric     value         sd
#>        <char> <char>     <num>      <num>
#> 1:   ensemble   RMSE 0.1725561 0.03171894
#> 2:      rpart   RMSE 0.1679543 0.04899049
#> 3:         rf   RMSE 0.1869204 0.03174027