# CORElearn v1.54.2

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## 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.

## Functions in CORElearn

 Name Description auxTest Test functions for manual usage attrEval Attribute evaluation cvGen Cross-validation and stratified cross-validation classPrototypes The typical instances of each class - class prototypes CORElearn-package R port of CORElearn destroyModels Destroy single model or all CORElearn models calibrate Calibration of probabilities according to the given prior. CoreModel Build a classification or regression model CORElearn-internal Internal structures of CORElearn C++ part classDataGen Artificial data for testing classification algorithms noEqualRows Number of equal rows in two data sets discretize Discretization of numeric attributes display.CoreModel Displaying decision and regression trees getRpartModel Conversion of a CoreModel tree into a rpart.object helpCore Description of parameters. modelEval Statistical evaluation of predictions getRFsizes Get sizes of the trees in RF infoCore Description of certain CORElearn parameters getCoreModel Conversion of model to a list ordDataGen Artificial data for testing ordEval algorithms regDataGen Artificial data for testing regression algorithms predict.CoreModel Prediction using constructed model rfClustering Random forest based clustering rfAttrEval Attribute evaluation with random forest reliabilityPlot Plots reliability plot of probabilities preparePlot Prepare graphics device rfOutliers Random forest based outlier detection ordEval Evaluation of ordered attributes rfOOB Out-of-bag performance estimation for random forests paramCoreIO Input/output of parameters from/to file saveRF Saves/loads random forests model to/from file testCore Verification of the CORElearn installation rfProximity A random forest based proximity function plot.ordEval Visualization of ordEval results plot.CoreModel Visualization of CoreModel models versionCore Package version No Results!