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caret (version 5.11-06)

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

231,168

Version

5.11-06

License

GPL-2

Maintainer

Max Kuhn

Last Published

January 13th, 2012

Functions in caret (5.11-06)

confusionMatrix.train

Estimate a Resampled Confusion Matrix
classDist

Compute and predict the distances to class centroids
lattice.rfe

Lattice functions for plotting resampling results of recursive feature selection
findLinearCombos

Determine linear combinations in a matrix
dhfr

Dihydrofolate Reductase Inhibitors Data
BloodBrain

Blood Brain Barrier Data
bag.default

A General Framework For Bagging
featurePlot

Wrapper for Lattice Plotting of Predictor Variables
lift

Lift Plot
histogram.train

Lattice functions for plotting resampling results
knnreg

k-Nearest Neighbour Regression
BoxCoxTrans.default

Box-Cox Transformations
postResample

Calculates performance across resamples
panel.needle

Needle Plot Lattice Panel
plot.varImp.train

Plotting variable importance measures
createGrid

Tuning Parameter Grid
spatialSign

Compute the multivariate spatial sign
predict.train

Extract predictions and class probabilities from train objects
train

Fit Predictive Models over Different Tuning Parameters
plotClassProbs

Plot Predicted Probabilities in Classification Models
pcaNNet.default

Neural Networks with a Principal Component Step
confusionMatrix

Create a confusion matrix
caret-internal

Internal Functions
normalize.AffyBatch.normalize2Reference

Quantile Normalization to a Reference Distribution
plsda

Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
caretSBF

Selection By Filtering (SBF) Helper Functions
summary.bagEarth

Summarize a bagged earth or FDA fit
mdrr

Multidrug Resistance Reversal (MDRR) Agent Data
createDataPartition

Data Splitting functions
avNNet.default

Neural Networks Using Model Averaging
dotPlot

Create a dotplot of variable importance values
sbfControl

Control Object for Selection By Filtering (SBF)
Alternate Affy Gene Expression Summary Methods.

Generate Expression Values from Probes
dummyVars

Create A Full Set of Dummy Variables
plot.train

Plot Method for the train Class
varImp

Calculation of variable importance for regression and classification models
diff.resamples

Inferential Assessments About Model Performance
nullModel

Fit a simple, non-informative model
caretFuncs

Backwards Feature Selection Helper Functions
xyplot.resamples

Lattice Functions for Visualizing Resampling Results
predict.bagEarth

Predicted values based on bagged Earth and FDA models
rfeControl

Controlling the Feature Selection Algorithms
resamples

Collation and Visualization of Resampling Results
sbf

Selection By Filtering (SBF)
print.train

Print Method for the train Class
predict.knnreg

Predictions from k-Nearest Neighbors Regression Model
segmentationData

Cell Body Segmentation
resampleSummary

Summary of resampled performance estimates
tecator

Fat, Water and Protein Content of Meat Samples
plotObsVsPred

Plot Observed versus Predicted Results in Regression and Classification Models
maxDissim

Maximum Dissimilarity Sampling
predictors

List predictors used in the model
roc

Compute the points for an ROC curve
normalize2Reference

Quantile Normalize Columns of a Matrix Based on a Reference Distribution
trainControl

Control parameters for train
as.table.confusionMatrix

Save Confusion Table Results
bagEarth

Bagged Earth
format.bagEarth

Format 'bagEarth' objects
prcomp.resamples

Principal Components Analysis of Resampling Results
GermanCredit

German Credit Data
preProcess

Pre-Processing of Predictors
bagFDA

Bagged FDA
sensitivity

Calculate sensitivity, specificity and predictive values
cars

Kelly Blue Book resale data for 2005 model year GM cars
filterVarImp

Calculation of filter-based variable importance
icr.formula

Independent Component Regression
panel.lift2

Lattice Panel Functions for Lift Plots
pottery

Pottery from Pre-Classical Sites in Italy
print.confusionMatrix

Print method for confusionMatrix
oneSE

Selecting tuning Parameters
rfe

Backwards Feature Selection
findCorrelation

Determine highly correlated variables
dotplot.diff.resamples

Lattice Functions for Visualizing Resampling Differences
predict.knn3

Predictions from k-Nearest Neighbors
knn3

k-Nearest Neighbour Classification
oil

Fatty acid composition of commercial oils
aucRoc

Compute the area under an ROC curve
nearZeroVar

Identification of near zero variance predictors
resampleHist

Plot the resampling distribution of the model statistics
cox2

COX-2 Activity Data
modelLookup

Descriptions Of Models Available in train()