Learn R Programming

⚠️There's a newer version (1.57.3) of this package.Take me there.

CORElearn (version 1.54.2)

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.

Copy Link

Version

Install

install.packages('CORElearn')

Monthly Downloads

3,065

Version

1.54.2

License

GPL-3

Maintainer

Marko Robnik-Sikonja

Last Published

February 8th, 2020

Functions in CORElearn (1.54.2)

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