Introduction to Machine Learning with R

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