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CORElearn (version 1.52.1)

Classification, Regression and Feature Evaluation

Description

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.

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Version

Install

install.packages('CORElearn')

Monthly Downloads

3,065

Version

1.52.1

License

GPL-3

Maintainer

Marko Robnik-Sikonja

Last Published

April 2nd, 2018

Functions in CORElearn (1.52.1)

display.CoreModel

Displaying decision and regression trees
modelEval

Statistical evaluation of predictions
rfAttrEval

Attribute evaluation with random forest
ordEval

Evaluation of ordered attributes
paramCoreIO

Input/output of parameters from/to file
rfClustering

Random forest based clustering
rfOOB

Out-of-bag performance estimation for random forests
rfOutliers

Random forest based outlier detection
reliabilityPlot

Plots reliability plot of probabilities
predict.CoreModel

Prediction using constructed model
regDataGen

Artificial data for testing regression algorithms
preparePlot

Prepare graphics device
plot.CoreModel

Visualization of CoreModel models
plot.ordEval

Visualization of ordEval results
versionCore

Package version
testCore

Verification of the CORElearn installation
saveRF

Saves/loads random forests model to/from file
rfProximity

A random forest based proximity function
cvGen

Cross-validation and stratified cross-validation
auxTest

Test functions for manual usage
classPrototypes

The typical instances of each class - class prototypes
classDataGen

Artificial data for testing classification algorithms
CORElearn-internal

Internal structures of CORElearn C++ part
calibrate

Calibration of probabilities according to the given prior.
destroyModels

Destroy single model or all CORElearn models
CoreModel

Build a classification or regression model
attrEval

Attribute evaluation
CORElearn-package

R port of CORElearn
noEqualRows

Number of equal rows in two data sets
getRpartModel

Conversion of a CoreModel tree into a rpart.object
infoCore

Description of certain CORElearn parameters
getRFsizes

Get sizes of the trees in RF
getCoreModel

Conversion of model to a list
ordDataGen

Artificial data for testing ordEval algorithms
discretize

Discretization of numeric attributes
helpCore

Description of parameters.