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caret (version 5.14-023)
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
138,220
Version
5.14-023
License
GPL-2
Maintainer
Max Kuhn
Last Published
February 23rd, 2012
Functions in caret (5.14-023)
Search all functions
BoxCoxTrans.default
Box-Cox Transformations
classDist
Compute and predict the distances to class centroids
GermanCredit
German Credit Data
cars
Kelly Blue Book resale data for 2005 model year GM cars
panel.needle
Needle Plot Lattice Panel
pcaNNet.default
Neural Networks with a Principal Component Step
preProcess
Pre-Processing of Predictors
findLinearCombos
Determine linear combinations in a matrix
predictors
List predictors used in the model
resamples
Collation and Visualization of Resampling Results
rfeControl
Controlling the Feature Selection Algorithms
resampleHist
Plot the resampling distribution of the model statistics
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
print.train
Print Method for the train Class
lift
Lift Plot
diff.resamples
Inferential Assessments About Model Performance
dotPlot
Create a dotplot of variable importance values
format.bagEarth
Format 'bagEarth' objects
resampleSummary
Summary of resampled performance estimates
findCorrelation
Determine highly correlated variables
as.table.confusionMatrix
Save Confusion Table Results
icr.formula
Independent Component Regression
confusionMatrix.train
Estimate a Resampled Confusion Matrix
knnreg
k-Nearest Neighbour Regression
modelLookup
Descriptions Of Models Available in train()
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
predict.train
Extract predictions and class probabilities from train objects
tecator
Fat, Water and Protein Content of Meat Samples
dummyVars
Create A Full Set of Dummy Variables
sbf
Selection By Filtering (SBF)
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
bag.default
A General Framework For Bagging
postResample
Calculates performance across resamples
prcomp.resamples
Principal Components Analysis of Resampling Results
BloodBrain
Blood Brain Barrier Data
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
knn3
k-Nearest Neighbour Classification
plot.train
Plot Method for the train Class
bagFDA
Bagged FDA
update.train
Update and Re-fit a Model
calibration
Probability Calibration Plot
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
createDataPartition
Data Splitting functions
histogram.train
Lattice functions for plotting resampling results
sensitivity
Calculate sensitivity, specificity and predictive values
sbfControl
Control Object for Selection By Filtering (SBF)
caretSBF
Selection By Filtering (SBF) Helper Functions
caretFuncs
Backwards Feature Selection Helper Functions
maxDissim
Maximum Dissimilarity Sampling
caret-internal
Internal Functions
predict.bagEarth
Predicted values based on bagged Earth and FDA models
createGrid
Tuning Parameter Grid
pottery
Pottery from Pre-Classical Sites in Italy
print.confusionMatrix
Print method for confusionMatrix
confusionMatrix
Create a confusion matrix
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
panel.lift2
Lattice Panel Functions for Lift Plots
spatialSign
Compute the multivariate spatial sign
varImp
Calculation of variable importance for regression and classification models
dhfr
Dihydrofolate Reductase Inhibitors Data
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
plotClassProbs
Plot Predicted Probabilities in Classification Models
segmentationData
Cell Body Segmentation
nullModel
Fit a simple, non-informative model
oil
Fatty acid composition of commercial oils
predict.knn3
Predictions from k-Nearest Neighbors
plot.varImp.train
Plotting variable importance measures
summary.bagEarth
Summarize a bagged earth or FDA fit
oneSE
Selecting tuning Parameters
nearZeroVar
Identification of near zero variance predictors
trainControl
Control parameters for train
bagEarth
Bagged Earth
avNNet.default
Neural Networks Using Model Averaging
cox2
COX-2 Activity Data
filterVarImp
Calculation of filter-based variable importance
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
rfe
Backwards Feature Selection