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caret (version 4.43)

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

4.43

License

GPL-2

Maintainer

Max Kuhn

Last Published

July 1st, 2010

Functions in caret (4.43)

confusionMatrix

Create a confusion matrix
predict.bagEarth

Predicted values based on bagged Earth and FDA models
predictors

List predictors used in the model
resamples

Collation and Visualization of Resampling Results
as.table.confusionMatrix

Save Confusion Table Results
knnreg

k-Nearest Neighbour Regression
Alternate Affy Gene Expression Summary Methods.

Generate Expression Values from Probes
preProcess

Pre-Processing of Predictors
nullModel

Fit a simple, non-informative model
featurePlot

Wrapper for Lattice Plotting of Predictor Variables
plotObsVsPred

Plot Observed versus Predicted Results in Regression and Classification Models
sbfControl

Control Object for Selection By Filtering (SBF)
normalize.AffyBatch.normalize2Reference

Quantile Normalization to a Reference Distribution
classDist

Compute and predict the distances to class centroids
caretFuncs

Backwards Feature Selection Helper Functions
aucRoc

Compute the area under an ROC curve
pottery

Pottery from Pre-Classical Sites in Italy
lattice.rfe

Lattice functions for plotting resampling results of recursive feature selection
spatialSign

Compute the multivariate spatial sign
caret-internal

Internal Functions
maxDissim

Maximum Dissimilarity Sampling
dhfr

Dihydrofolate Reductase Inhibitors Data
bagEarth

Bagged Earth
panel.needle

Needle Plot Lattice Panel
diff.resamples

Inferential Assessments About Model Performance
cox2

COX-2 Activity Data
nearZeroVar

Identification of near zero variance predictors
cars

Kelly Blue Book resale data for 2005 model year GM cars
predict.knnreg

Predictions from k-Nearest Neighbors Regression Model
postResample

Calculates performance across resamples
plotClassProbs

Plot Predicted Probabilities in Classification Models
resampleHist

Plot the resampling distribution of the model statistics
dotplot.diff.resamples

Lattice Functions for Visualizing Resampling Differences
sbf

Selection By Filtering (SBF)
createGrid

Tuning Parameter Grid
applyProcessing

Data Processing on Predictor Variables (Deprecated)
predict.train

Extract predictions and class probabilities from train objects
histogram.train

Lattice functions for plotting resampling results
plot.varImp.train

Plotting variable importance measures
findLinearCombos

Determine linear combinations in a matrix
normalize2Reference

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

Fatty acid composition of commercial oils
rfeControl

Controlling the Feature Selection Algorithms
resampleSummary

Summary of resampled performance estimates
createDataPartition

Data Splitting functions
dotPlot

Create a dotplot of variable importance values
oneSE

Selecting tuning Parameters
print.train

Print Method for the train Class
tecator

Fat, Water and Protein Content of Meat Samples
bagFDA

Bagged FDA
format.bagEarth

Format 'bagEarth' objects
print.confusionMatrix

Print method for confusionMatrix
train

Fit Predictive Models over Different Tuning Parameters
sensitivity

Calculate sensitivity, specificity and predictive values
summary.bagEarth

Summarize a bagged earth or FDA fit
icr.formula

Independent Component Regression
filterVarImp

Calculation of filter-based variable importance
pcaNNet.default

Neural Networks with a Principal Component Step
mdrr

Multidrug Resistance Reversal (MDRR) Agent Data
predict.knn3

Predictions from k-Nearest Neighbors
roc

Compute the points for an ROC curve
caretSBF

Selection By Filtering (SBF) Helper Functions
rfe

Backwards Feature Selection
BloodBrain

Blood Brain Barrier Data
findCorrelation

Determine highly correlated variables
knn3

k-Nearest Neighbour Classification
xyplot.resamples

Lattice Functions for Visualizing Resampling Results
plot.train

Plot Method for the train Class
plsda

Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
varImp

Calculation of variable importance for regression and classification models
trainControl

Control parameters for train