Appendix A. Encyclopedia of Machine Learning Models in caret

Although this list is long, it is by no means completely comprehensive. These are machine learning algorithms that we use with the caret package discussed in this book in more detail. One of the major powers of caret is that it gives you the ability to switch very quickly from using, for example, a random forest machine learning algorithm to a neural network. With caret, all we would need to do is change rf in our model to nnet. This appendix provides a reference to look up all of the available machine learning algorithm calls, what libraries they depend on, an overall description or label, and their model type (regression, classification, or both).

Table A-1. Machine learning algorithms in caret
Algorithm name Library dependencies Label Type

ada

ada, plyr

Boosted Classification Trees

Classification

AdaBag

adabag, plyr

Bagged AdaBoost

Classification

AdaBoost.M1

adabag, plyr

AdaBoost.M1

Classification

adaboost

fastAdaboost

AdaBoost Classification Trees

Classification

amdai

adaptDA

Adaptive-Mixture Discriminant Analysis

Classification

ANFIS

frbs

Adaptive-Network-Based Fuzzy Inference System

Regression

avNNet

nnet

Model-Averaged Neural Network

Both

awnb

bnclassify

Naive Bayes Classifier with Attribute Weighting

Classification

awtan

bnclassify

Tree-Augmented Naive Bayes Classifier with Attribute Weighting

Classification

bag

caret

Bagged Model

Both

bagEarth

earth

Bagged MARS

Both

bagEarthGCV

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