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caret (version 4.89)

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

4.89

License

GPL-2

Maintainer

Max Kuhn

Last Published

May 28th, 2011

Functions in caret (4.89)

bag.default

A General Framework For Bagging
diff.resamples

Inferential Assessments About Model Performance
BloodBrain

Blood Brain Barrier Data
dotplot.diff.resamples

Lattice Functions for Visualizing Resampling Differences
GermanCredit

German Credit Data
caret-internal

Internal Functions
as.table.confusionMatrix

Save Confusion Table Results
applyProcessing

Data Processing on Predictor Variables (Deprecated)
histogram.train

Lattice functions for plotting resampling results
classDist

Compute and predict the distances to class centroids
modelLookup

Descriptions Of Models Available in train()
knn3

k-Nearest Neighbour Classification
dotPlot

Create a dotplot of variable importance values
dhfr

Dihydrofolate Reductase Inhibitors Data
findCorrelation

Determine highly correlated variables
featurePlot

Wrapper for Lattice Plotting of Predictor Variables
knnreg

k-Nearest Neighbour Regression
normalize.AffyBatch.normalize2Reference

Quantile Normalization to a Reference Distribution
icr.formula

Independent Component Regression
createDataPartition

Data Splitting functions
cox2

COX-2 Activity Data
plotClassProbs

Plot Predicted Probabilities in Classification Models
confusionMatrix

Create a confusion matrix
findLinearCombos

Determine linear combinations in a matrix
pcaNNet.default

Neural Networks with a Principal Component Step
nearZeroVar

Identification of near zero variance predictors
lattice.rfe

Lattice functions for plotting resampling results of recursive feature selection
predict.train

Extract predictions and class probabilities from train objects
normalize2Reference

Quantile Normalize Columns of a Matrix Based on a Reference Distribution
predict.bagEarth

Predicted values based on bagged Earth and FDA models
preProcess

Pre-Processing of Predictors
nullModel

Fit a simple, non-informative model
xyplot.resamples

Lattice Functions for Visualizing Resampling Results
postResample

Calculates performance across resamples
maxDissim

Maximum Dissimilarity Sampling
mdrr

Multidrug Resistance Reversal (MDRR) Agent Data
predict.knn3

Predictions from k-Nearest Neighbors
bagFDA

Bagged FDA
predict.knnreg

Predictions from k-Nearest Neighbors Regression Model
cars

Kelly Blue Book resale data for 2005 model year GM cars
panel.needle

Needle Plot Lattice Panel
caretSBF

Selection By Filtering (SBF) Helper Functions
aucRoc

Compute the area under an ROC curve
rfe

Backwards Feature Selection
plot.varImp.train

Plotting variable importance measures
varImp

Calculation of variable importance for regression and classification models
train

Fit Predictive Models over Different Tuning Parameters
sensitivity

Calculate sensitivity, specificity and predictive values
plot.train

Plot Method for the train Class
summary.bagEarth

Summarize a bagged earth or FDA fit
caretFuncs

Backwards Feature Selection Helper Functions
Alternate Affy Gene Expression Summary Methods.

Generate Expression Values from Probes
format.bagEarth

Format 'bagEarth' objects
prcomp.resamples

Principal Components Analysis of Resampling Results
resampleSummary

Summary of resampled performance estimates
print.confusionMatrix

Print method for confusionMatrix
print.train

Print Method for the train Class
resampleHist

Plot the resampling distribution of the model statistics
plotObsVsPred

Plot Observed versus Predicted Results in Regression and Classification Models
resamples

Collation and Visualization of Resampling Results
bagEarth

Bagged Earth
dummyVars

Create A Full Set of Dummy Variables
tecator

Fat, Water and Protein Content of Meat Samples
pottery

Pottery from Pre-Classical Sites in Italy
trainControl

Control parameters for train
sbf

Selection By Filtering (SBF)
plsda

Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
predictors

List predictors used in the model
rfeControl

Controlling the Feature Selection Algorithms
createGrid

Tuning Parameter Grid
oneSE

Selecting tuning Parameters
filterVarImp

Calculation of filter-based variable importance
spatialSign

Compute the multivariate spatial sign
roc

Compute the points for an ROC curve
sbfControl

Control Object for Selection By Filtering (SBF)
oil

Fatty acid composition of commercial oils
BoxCoxTrans.default

Box-Cox Transformations
segmentationData

Cell Body Segmentation