CORElearn - Classification, Regression and Feature Evaluation
A suite of machine learning algorithms written in C++ with
the R interface contains several learning techniques for
classification and regression. Predictive models include e.g.,
classification and regression trees with optional constructive
induction and models in the leaves, random forests, kNN, naive
Bayes, and locally weighted regression. All predictions
obtained with these models can be explained and visualized with
the 'ExplainPrediction' package. This package is especially
strong in feature evaluation where it contains several variants
of Relief algorithm and many impurity based attribute
evaluation functions, e.g., Gini, information gain, MDL, and
DKM. These methods can be used for feature selection or
discretization of numeric attributes. The OrdEval algorithm and
its visualization is used for evaluation of data sets with
ordinal features and class, enabling analysis according to the
Kano model of customer satisfaction. Several algorithms support
parallel multithreaded execution via OpenMP. The top-level
documentation is reachable through ?CORElearn.
