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caret (version 5.07-005)

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

163,965

Version

5.07-005

License

GPL-2

Maintainer

Max Kuhn

Last Published

November 8th, 2011

Functions in caret (5.07-005)

bagFDA

Bagged FDA
cars

Kelly Blue Book resale data for 2005 model year GM cars
caret-internal

Internal Functions
GermanCredit

German Credit Data
BoxCoxTrans.default

Box-Cox Transformations
avNNet.default

Neural Networks Using Model Averaging
bagEarth

Bagged Earth
as.table.confusionMatrix

Save Confusion Table Results
confusionMatrix.train

Estimate a Resampled Confusion Matrix
classDist

Compute and predict the distances to class centroids
confusionMatrix

Create a confusion matrix
dhfr

Dihydrofolate Reductase Inhibitors Data
cox2

COX-2 Activity Data
createGrid

Tuning Parameter Grid
diff.resamples

Inferential Assessments About Model Performance
dotPlot

Create a dotplot of variable importance values
BloodBrain

Blood Brain Barrier Data
createDataPartition

Data Splitting functions
aucRoc

Compute the area under an ROC curve
bag.default

A General Framework For Bagging
dummyVars

Create A Full Set of Dummy Variables
featurePlot

Wrapper for Lattice Plotting of Predictor Variables
filterVarImp

Calculation of filter-based variable importance
predict.train

Extract predictions and class probabilities from train objects
icr.formula

Independent Component Regression
Alternate Affy Gene Expression Summary Methods.

Generate Expression Values from Probes
knnreg

k-Nearest Neighbour Regression
format.bagEarth

Format 'bagEarth' objects
findLinearCombos

Determine linear combinations in a matrix
xyplot.resamples

Lattice Functions for Visualizing Resampling Results
knn3

k-Nearest Neighbour Classification
histogram.train

Lattice functions for plotting resampling results
lattice.rfe

Lattice functions for plotting resampling results of recursive feature selection
dotplot.diff.resamples

Lattice Functions for Visualizing Resampling Differences
maxDissim

Maximum Dissimilarity Sampling
normalize.AffyBatch.normalize2Reference

Quantile Normalization to a Reference Distribution
mdrr

Multidrug Resistance Reversal (MDRR) Agent Data
oil

Fatty acid composition of commercial oils
lift

Lift Plot
pcaNNet.default

Neural Networks with a Principal Component Step
postResample

Calculates performance across resamples
modelLookup

Descriptions Of Models Available in train()
plot.varImp.train

Plotting variable importance measures
plotClassProbs

Plot Predicted Probabilities in Classification Models
plsda

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

Plot Method for the train Class
panel.lift2

Lattice Panel Functions for Lift Plots
pottery

Pottery from Pre-Classical Sites in Italy
prcomp.resamples

Principal Components Analysis of Resampling Results
plotObsVsPred

Plot Observed versus Predicted Results in Regression and Classification Models
findCorrelation

Determine highly correlated variables
panel.needle

Needle Plot Lattice Panel
predict.bagEarth

Predicted values based on bagged Earth and FDA models
predict.knn3

Predictions from k-Nearest Neighbors
preProcess

Pre-Processing of Predictors
predictors

List predictors used in the model
resampleHist

Plot the resampling distribution of the model statistics
predict.knnreg

Predictions from k-Nearest Neighbors Regression Model
print.train

Print Method for the train Class
print.confusionMatrix

Print method for confusionMatrix
resampleSummary

Summary of resampled performance estimates
nullModel

Fit a simple, non-informative model
normalize2Reference

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

Compute the points for an ROC curve
caretFuncs

Backwards Feature Selection Helper Functions
rfeControl

Controlling the Feature Selection Algorithms
rfe

Backwards Feature Selection
resamples

Collation and Visualization of Resampling Results
sbf

Selection By Filtering (SBF)
spatialSign

Compute the multivariate spatial sign
trainControl

Control parameters for train
summary.bagEarth

Summarize a bagged earth or FDA fit
nearZeroVar

Identification of near zero variance predictors
caretSBF

Selection By Filtering (SBF) Helper Functions
oneSE

Selecting tuning Parameters
sensitivity

Calculate sensitivity, specificity and predictive values
sbfControl

Control Object for Selection By Filtering (SBF)
train

Fit Predictive Models over Different Tuning Parameters
varImp

Calculation of variable importance for regression and classification models
segmentationData

Cell Body Segmentation
tecator

Fat, Water and Protein Content of Meat Samples