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
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Last month downloads


Date 2020-02-08
License GPL-3
NeedsCompilation yes
Repository CRAN
Packaged 2020-02-08 09:47:26 UTC; rmarko
Date/Publication 2020-02-08 10:20:07 UTC

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