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caret (version 4.69)

Classification and Regression Training

Description

Misc functions for training and plotting classification and regression models

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install.packages('caret')

Monthly Downloads

208,749

Version

4.69

License

GPL-2

Maintainer

Last Published

October 29th, 2010

Functions in caret (4.69)

bagEarth

Bagged Earth
dotPlot

Create a dotplot of variable importance values
icr.formula

Independent Component Regression
knn3

k-Nearest Neighbour Classification
normalize2Reference

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

Plot Predicted Probabilities in Classification Models
print.train

Print Method for the train Class
resampleHist

Plot the resampling distribution of the model statistics
resampleSummary

Summary of resampled performance estimates
segmentationData

Cell Body Segmentation
oneSE

Selecting tuning Parameters
tecator

Fat, Water and Protein Content of Meat Samples
GermanCredit

German Credit Data
confusionMatrix

Create a confusion matrix
Alternate Affy Gene Expression Summary Methods.

Generate Expression Values from Probes
plotObsVsPred

Plot Observed versus Predicted Results in Regression and Classification Models
summary.bagEarth

Summarize a bagged earth or FDA fit
applyProcessing

Data Processing on Predictor Variables (Deprecated)
BloodBrain

Blood Brain Barrier Data
xyplot.resamples

Lattice Functions for Visualizing Resampling Results
knnreg

k-Nearest Neighbour Regression
panel.needle

Needle Plot Lattice Panel
pcaNNet.default

Neural Networks with a Principal Component Step
postResample

Calculates performance across resamples
roc

Compute the points for an ROC curve
sensitivity

Calculate sensitivity, specificity and predictive values
trainControl

Control parameters for train
format.bagEarth

Format 'bagEarth' objects
classDist

Compute and predict the distances to class centroids
createDataPartition

Data Splitting functions
caretFuncs

Backwards Feature Selection Helper Functions
sbfControl

Control Object for Selection By Filtering (SBF)
filterVarImp

Calculation of filter-based variable importance
findLinearCombos

Determine linear combinations in a matrix
cox2

COX-2 Activity Data
createGrid

Tuning Parameter Grid
featurePlot

Wrapper for Lattice Plotting of Predictor Variables
findCorrelation

Determine highly correlated variables
predict.knn3

Predictions from k-Nearest Neighbors
dotplot.diff.resamples

Lattice Functions for Visualizing Resampling Differences
modelLookup

Descriptions Of Models Available in train()
preProcess

Pre-Processing of Predictors
caret-internal

Internal Functions
resamples

Collation and Visualization of Resampling Results
histogram.train

Lattice functions for plotting resampling results
diff.resamples

Inferential Assessments About Model Performance
spatialSign

Compute the multivariate spatial sign
nullModel

Fit a simple, non-informative model
caretSBF

Selection By Filtering (SBF) Helper Functions
as.table.confusionMatrix

Save Confusion Table Results
maxDissim

Maximum Dissimilarity Sampling
cars

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

Predictions from k-Nearest Neighbors Regression Model
rfeControl

Controlling the Feature Selection Algorithms
pottery

Pottery from Pre-Classical Sites in Italy
predict.bagEarth

Predicted values based on bagged Earth and FDA models
print.confusionMatrix

Print method for confusionMatrix
plot.varImp.train

Plotting variable importance measures
rfe

Backwards Feature Selection
nearZeroVar

Identification of near zero variance predictors
plsda

Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
normalize.AffyBatch.normalize2Reference

Quantile Normalization to a Reference Distribution
predictors

List predictors used in the model
oil

Fatty acid composition of commercial oils
train

Fit Predictive Models over Different Tuning Parameters
varImp

Calculation of variable importance for regression and classification models
predict.train

Extract predictions and class probabilities from train objects
lattice.rfe

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

Plot Method for the train Class
bag.default

A General Framework For Bagging
prcomp.resamples

Principal Components Analysis of Resampling Results
mdrr

Multidrug Resistance Reversal (MDRR) Agent Data
dhfr

Dihydrofolate Reductase Inhibitors Data
aucRoc

Compute the area under an ROC curve
sbf

Selection By Filtering (SBF)
bagFDA

Bagged FDA