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caret (version 4.12)

Classification and Regression Training

Description

Misc functions for training and plotting classification and regression models

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Version

Install

install.packages('caret')

Monthly Downloads

230,598

Version

4.12

License

GPL-2

Maintainer

Max Kuhn

Last Published

May 8th, 2009

Functions in caret (4.12)

as.table.confusionMatrix

Save Confusion Table Results
BloodBrain

Blood Brain Barrier Data
aucRoc

Compute the area under an ROC curve
confusionMatrix

Create a confusion matrix
createDataPartition

Data Splitting functions
format.bagEarth

Format 'bagEarth' objects
featurePlot

Wrapper for Lattice Plotting of Predictor Variables
Alternate Affy Gene Expression Summary Methods.

Generate Expression Values from Probes
cox2

COX-2 Activity Data
createGrid

Tuning Parameter Grid
applyProcessing

Data Processing on Predictor Variables (Deprecated)
pcaNNet.default

Neural Networks with a Principal Component Step
findLinearCombos

Determine linear combinations in a matrix
caret-internal

Internal Functions
maxDissim

Maximum Dissimilarity Sampling
bagEarth

Bagged Earth
oil

Fatty acid composition of commercial oils
mdrr

Multidrug Resistance Reversal (MDRR) Agent Data
filterVarImp

Calculation of filter-based variable importance
panel.needle

Needle Plot Lattice Panel
normalize2Reference

Quantile Normalize Columns of a Matrix Based on a Reference Distribution
lattice.rfe

Lattice functions for plotting resampling results of recursive feature selection
dotPlot

Create a dotplot of variable importance values
knn3

k-Nearest Neighbour Classification
predict.train

Extract predictions and class probabilities from train objects
histogram.train

Lattice functions for plotting resampling results
roc

Compute the points for an ROC curve
plotObsVsPred

Plot Observed versus Predicted Results in Regression and Classification Models
plot.train

Plot Method for the train Class
rfeControl

Controlling the Feature Selection Algorithms
classDist

Compute and predict the distances to class centroids
bagFDA

Bagged FDA
spatialSign

Compute the multivariate spatial sign
plotClassProbs

Plot Predicted Probabilities in Classification Models
rfe

Backwards Feature Selection
findCorrelation

Determine highly correlated variables
oneSE

Selecting tuning Parameters
resampleHist

Plot the resampling distribution of the model statistics
postResample

Calculates performance across resamples
trainControl

Control parameters for train
resampleSummary

Summary of resampled performance estimates
predict.bagEarth

Predicted values based on bagged Earth and FDA models
pottery

Pottery from Pre-Classical Sites in Italy
plot.varImp.train

Plotting variable importance measures
predict.knnreg

Predictions from k-Nearest Neighbors Regression Model
tecator

Fat, Water and Protein Content of Maat Samples
summary.bagEarth

Summarize a bagged earth or FDA fit
sensitivity

Calculate sensitivity, specificity and predictive values
predictors

List predictors used in the model
predict.knn3

Predictions from k-Nearest Neighbors
preProcess

Pre-Processing of Predictors
print.confusionMatrix

Print method for confusionMatrix
normalize.AffyBatch.normalize2Reference

Quantile Normalization to a Reference Distribution
train

Fit Predictive Models over Different Tuning Parameters
plsda

Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
print.train

Print Method for the train Class
nearZeroVar

Identification of near zero variance predictors
knnreg

k-Nearest Neighbour Regression
varImp

Calculation of variable importance for regression and classification models