Description Author(s) See Also
An R package for building binary and multiclass decision tree algorithms using generalized entropy functions, such as Renyi, Tsallis, Sharma-Mittal, Sharma-Taneja and Kapur, to measure impurity of a node. These are important extensions of the existing algorithms which usually employ Shannon entropy and the concept of information gain. Additionally, ImbTreeEntropy is able to handle imbalanced data which is a challenging issue in many practical applications. The package supports cost-sensitive learning by defining a misclassification cost matrix and weight sensitive learning. It accepts all types of attributes, including continuous, ordered and nominal.
Krzysztof Gajowniczek
Maintainer: krzysztof_gajowniczek@sggw.edu.pl
ImbTreeEntropy
, ImbTreeEntropyInter
, PredictTree
, PrintTree
, PrintTreeInter
, ExtractRules
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