CORElearn1.57.3 package

Classification, Regression and Feature Evaluation

attrEval

Attribute evaluation

auxTest

Test functions for manual usage

calibrate

Calibration of probabilities according to the given prior.

classDataGen

Artificial data for testing classification algorithms

classPrototypes

The typical instances of each class - class prototypes

CORElearn-internal

Internal structures of CORElearn C++ part

CORElearn-package

R port of CORElearn

CoreModel

Build a classification or regression model

cvGen

Cross-validation and stratified cross-validation

destroyModels

Destroy single model or all CORElearn models

discretize

Discretization of numeric attributes

display.CoreModel

Displaying decision and regression trees

getCoreModel

Conversion of model to a list

getRFsizes

Get sizes of the trees in RF

getRpartModel

Conversion of a CoreModel tree into a rpart.object

helpCore

Description of parameters.

infoCore

Description of certain CORElearn parameters

modelEval

Statistical evaluation of predictions

noEqualRows

Number of equal rows in two data sets

ordDataGen

Artificial data for testing ordEval algorithms

ordEval

Evaluation of ordered attributes

paramCoreIO

Input/output of parameters from/to file

plot.CoreModel

Visualization of CoreModel models

plot.ordEval

Visualization of ordEval results

predict.CoreModel

Prediction using constructed model

preparePlot

Prepare graphics device

regDataGen

Artificial data for testing regression algorithms

reliabiltyPlot

Plots reliability plot of probabilities

rfAttrEval

Attribute evaluation with random forest

rfClustering

Random forest based clustering

rfOOB

Out-of-bag performance estimation for random forests

rfOutliers

Random forest based outlier detection

rfProximity

A random forest based proximity function

saveRF

Saves/loads random forests model to/from file

testCore

Verification of the CORElearn installation

versionCore

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.