Recent Changes to caret

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Here is a summary of some recent changes to caret.

Feature Updates:

• train was updated to utilize recent changes in the gbm package that allow for boosting with three or more classes (via the multinomial distribution)

• The Yeo-Johnson power transformation was added. This is very similar to the Box-Cox transformation, but it does not require the data to be greater than zero.

New models referenced by train:

• Maximum uncertainty linear discriminant analysis (Mlda) and factor-based linear discriminant analysis (RFlda) from the HiDimDA package were added.

• The kknn.train model in the kknn package was added. This is basically a more intelligent K-nearest neighbors model that can use distance weighting, non-Euclidean distances (via the o Minkowski distance) and a few other features.

• The extraTrees function in the package of the same name was added. This generalizes the random forest model by adding randomness to the predictors and the split values that are evaluated at each split point.

Numerous bugs were also fixed in the last few releases.

The new version is 5.16-04. Feel free to email me at mxkuhn@gmail.com if you have any feature requests or questions.

(This article was originally posted at http://appliedpredictivemodeling.com)