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caret (version 4.31)
Classification and Regression Training
Description
Misc functions for training and plotting classification and regression models
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Install
install.packages('caret')
Monthly Downloads
163,965
Version
4.31
License
GPL-2
Maintainer
Max Kuhn
Last Published
December 9th, 2009
Functions in caret (4.31)
Search all functions
classDist
Compute and predict the distances to class centroids
cox2
COX-2 Activity Data
createDataPartition
Data Splitting functions
filterVarImp
Calculation of filter-based variable importance
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
preProcess
Pre-Processing of Predictors
predict.bagEarth
Predicted values based on bagged Earth and FDA models
resampleHist
Plot the resampling distribution of the model statistics
rfe
Backwards Feature Selection
rfeControl
Controlling the Feature Selection Algorithms
histogram.train
Lattice functions for plotting resampling results
train
Fit Predictive Models over Different Tuning Parameters
applyProcessing
Data Processing on Predictor Variables (Deprecated)
print.train
Print Method for the train Class
sbfControl
Control Object for Selection By Filtering (SBF)
postResample
Calculates performance across resamples
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
knnreg
k-Nearest Neighbour Regression
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
print.confusionMatrix
Print method for confusionMatrix
predictors
List predictors used in the model
plot.train
Plot Method for the train Class
caretFuncs
Backwards Feature Selection Helper Functions
oil
Fatty acid composition of commercial oils
bagEarth
Bagged Earth
caretSBF
Selection By Filtering (SBF) Helper Functions
roc
Compute the points for an ROC curve
createGrid
Tuning Parameter Grid
panel.needle
Needle Plot Lattice Panel
oneSE
Selecting tuning Parameters
summary.bagEarth
Summarize a bagged earth or FDA fit
plotClassProbs
Plot Predicted Probabilities in Classification Models
as.table.confusionMatrix
Save Confusion Table Results
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
format.bagEarth
Format 'bagEarth' objects
sensitivity
Calculate sensitivity, specificity and predictive values
caret-internal
Internal Functions
nearZeroVar
Identification of near zero variance predictors
maxDissim
Maximum Dissimilarity Sampling
BloodBrain
Blood Brain Barrier Data
varImp
Calculation of variable importance for regression and classification models
tecator
Fat, Water and Protein Content of Maat Samples
trainControl
Control parameters for train
aucRoc
Compute the area under an ROC curve
findCorrelation
Determine highly correlated variables
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
bagFDA
Bagged FDA
findLinearCombos
Determine linear combinations in a matrix
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
nullModel
Fit a simple, non-informative model
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
resampleSummary
Summary of resampled performance estimates
sbf
Selection By Filtering (SBF)
spatialSign
Compute the multivariate spatial sign
pcaNNet.default
Neural Networks with a Principal Component Step
confusionMatrix
Create a confusion matrix
predict.train
Extract predictions and class probabilities from train objects
dotPlot
Create a dotplot of variable importance values
plot.varImp.train
Plotting variable importance measures
pottery
Pottery from Pre-Classical Sites in Italy
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
predict.knn3
Predictions from k-Nearest Neighbors
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
knn3
k-Nearest Neighbour Classification