Classification, Regression and Feature Evaluation
Attribute evaluation
Test functions for manual usage
Calibration of probabilities according to the given prior.
Artificial data for testing classification algorithms
The typical instances of each class - class prototypes
Internal structures of CORElearn C++ part
R port of CORElearn
Build a classification or regression model
Cross-validation and stratified cross-validation
Destroy single model or all CORElearn models
Discretization of numeric attributes
Displaying decision and regression trees
Conversion of model to a list
Get sizes of the trees in RF
Conversion of a CoreModel tree into a rpart.object
Description of parameters.
Description of certain CORElearn parameters
Statistical evaluation of predictions
Number of equal rows in two data sets
Artificial data for testing ordEval algorithms
Evaluation of ordered attributes
Input/output of parameters from/to file
Visualization of CoreModel models
Visualization of ordEval results
Prediction using constructed model
Prepare graphics device
Artificial data for testing regression algorithms
Plots reliability plot of probabilities
Attribute evaluation with random forest
Random forest based clustering
Out-of-bag performance estimation for random forests
Random forest based outlier detection
A random forest based proximity function
Saves/loads random forests model to/from file
Verification of the CORElearn installation
Package version
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.