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caret (version 5.16-04)

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

5.16-04

License

GPL-2

Maintainer

Max Kuhn

Last Published

May 17th, 2013

Functions in caret (5.16-04)

calibration

Probability Calibration Plot
icr.formula

Independent Component Regression
bag.default

A General Framework For Bagging
predict.knnreg

Predictions from k-Nearest Neighbors Regression Model
plotClassProbs

Plot Predicted Probabilities in Classification Models
dotplot.diff.resamples

Lattice Functions for Visualizing Resampling Differences
findLinearCombos

Determine linear combinations in a matrix
createGrid

Tuning Parameter Grid
createDataPartition

Data Splitting functions
BloodBrain

Blood Brain Barrier Data
predictors

List predictors used in the model
GermanCredit

German Credit Data
dotPlot

Create a dotplot of variable importance values
knn3

k-Nearest Neighbour Classification
sbfControl

Control Object for Selection By Filtering (SBF)
sensitivity

Calculate sensitivity, specificity and predictive values
knnreg

k-Nearest Neighbour Regression
update.train

Update and Re-fit a Model
classDist

Compute and predict the distances to class centroids
confusionMatrix.train

Estimate a Resampled Confusion Matrix
caretFuncs

Backwards Feature Selection Helper Functions
filterVarImp

Calculation of filter-based variable importance
dhfr

Dihydrofolate Reductase Inhibitors Data
downSample

Down- and Up-Sampling Imbalanced Data
maxDissim

Maximum Dissimilarity Sampling
dummyVars

Create A Full Set of Dummy Variables
mdrr

Multidrug Resistance Reversal (MDRR) Agent Data
panel.lift2

Lattice Panel Functions for Lift Plots
caret-internal

Internal Functions
rfeControl

Controlling the Feature Selection Algorithms
plotObsVsPred

Plot Observed versus Predicted Results in Regression and Classification Models
sbf

Selection By Filtering (SBF)
xyplot.resamples

Lattice Functions for Visualizing Resampling Results
segmentationData

Cell Body Segmentation
confusionMatrix

Create a confusion matrix
bagFDA

Bagged FDA
nullModel

Fit a simple, non-informative model
lattice.rfe

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

Inferential Assessments About Model Performance
prcomp.resamples

Principal Components Analysis of Resampling Results
cox2

COX-2 Activity Data
histogram.train

Lattice functions for plotting resampling results
resamples

Collation and Visualization of Resampling Results
modelLookup

Descriptions Of Models Available in train()
rfe

Backwards Feature Selection
plot.train

Plot Method for the train Class
Alternate Affy Gene Expression Summary Methods.

Generate Expression Values from Probes
oil

Fatty acid composition of commercial oils
preProcess

Pre-Processing of Predictors
predict.train

Extract predictions and class probabilities from train objects
as.table.confusionMatrix

Save Confusion Table Results
nearZeroVar

Identification of near zero variance predictors
summary.bagEarth

Summarize a bagged earth or FDA fit
predict.knn3

Predictions from k-Nearest Neighbors
tecator

Fat, Water and Protein Content of Meat Samples
resampleHist

Plot the resampling distribution of the model statistics
normalize2Reference

Quantile Normalize Columns of a Matrix Based on a Reference Distribution
pcaNNet.default

Neural Networks with a Principal Component Step
print.train

Print Method for the train Class
twoClassSim

Two-Class Simulations
plot.varImp.train

Plotting variable importance measures
trainControl

Control parameters for train
oneSE

Selecting tuning Parameters
varImp

Calculation of variable importance for regression and classification models
featurePlot

Wrapper for Lattice Plotting of Predictor Variables
BoxCoxTrans.default

Box-Cox Transformations
plsda

Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
caretSBF

Selection By Filtering (SBF) Helper Functions
spatialSign

Compute the multivariate spatial sign
findCorrelation

Determine highly correlated variables
bagEarth

Bagged Earth
cars

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

Summary of resampled performance estimates
pottery

Pottery from Pre-Classical Sites in Italy
panel.needle

Needle Plot Lattice Panel
predict.bagEarth

Predicted values based on bagged Earth and FDA models
avNNet.default

Neural Networks Using Model Averaging
format.bagEarth

Format 'bagEarth' objects
lift

Lift Plot
normalize.AffyBatch.normalize2Reference

Quantile Normalization to a Reference Distribution
postResample

Calculates performance across resamples
print.confusionMatrix

Print method for confusionMatrix