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caret (version 4.57)
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
186,340
Version
4.57
License
GPL-2
Maintainer
Max Kuhn
Last Published
August 26th, 2010
Functions in caret (4.57)
Search all functions
bagEarth
Bagged Earth
filterVarImp
Calculation of filter-based variable importance
postResample
Calculates performance across resamples
panel.needle
Needle Plot Lattice Panel
rfe
Backwards Feature Selection
predict.bagEarth
Predicted values based on bagged Earth and FDA models
preProcess
Pre-Processing of Predictors
bagFDA
Bagged FDA
format.bagEarth
Format 'bagEarth' objects
knnreg
k-Nearest Neighbour Regression
dotPlot
Create a dotplot of variable importance values
maxDissim
Maximum Dissimilarity Sampling
resampleHist
Plot the resampling distribution of the model statistics
sbf
Selection By Filtering (SBF)
oil
Fatty acid composition of commercial oils
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
GermanCredit
German Credit Data
createGrid
Tuning Parameter Grid
caretFuncs
Backwards Feature Selection Helper Functions
cars
Kelly Blue Book resale data for 2005 model year GM cars
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
applyProcessing
Data Processing on Predictor Variables (Deprecated)
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
predictors
List predictors used in the model
varImp
Calculation of variable importance for regression and classification models
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
diff.resamples
Inferential Assessments About Model Performance
findLinearCombos
Determine linear combinations in a matrix
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
findCorrelation
Determine highly correlated variables
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
cox2
COX-2 Activity Data
nearZeroVar
Identification of near zero variance predictors
pcaNNet.default
Neural Networks with a Principal Component Step
histogram.train
Lattice functions for plotting resampling results
sensitivity
Calculate sensitivity, specificity and predictive values
resampleSummary
Summary of resampled performance estimates
BloodBrain
Blood Brain Barrier Data
as.table.confusionMatrix
Save Confusion Table Results
dhfr
Dihydrofolate Reductase Inhibitors Data
plot.train
Plot Method for the train Class
plot.varImp.train
Plotting variable importance measures
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
trainControl
Control parameters for train
resamples
Collation and Visualization of Resampling Results
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
classDist
Compute and predict the distances to class centroids
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
predict.knn3
Predictions from k-Nearest Neighbors
train
Fit Predictive Models over Different Tuning Parameters
pottery
Pottery from Pre-Classical Sites in Italy
predict.train
Extract predictions and class probabilities from train objects
knn3
k-Nearest Neighbour Classification
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
prcomp.resamples
Principal Components Analysis of Resampling Results
print.train
Print Method for the train Class
caretSBF
Selection By Filtering (SBF) Helper Functions
summary.bagEarth
Summarize a bagged earth or FDA fit
tecator
Fat, Water and Protein Content of Meat Samples
sbfControl
Control Object for Selection By Filtering (SBF)
rfeControl
Controlling the Feature Selection Algorithms
modelLookup
Descriptions Of Models Available in train()
bag.default
A General Framework For Bagging
caret-internal
Internal Functions
confusionMatrix
Create a confusion matrix
nullModel
Fit a simple, non-informative model
print.confusionMatrix
Print method for confusionMatrix
roc
Compute the points for an ROC curve
oneSE
Selecting tuning Parameters
plotClassProbs
Plot Predicted Probabilities in Classification Models
aucRoc
Compute the area under an ROC curve
createDataPartition
Data Splitting functions
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
spatialSign
Compute the multivariate spatial sign
icr.formula
Independent Component Regression