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caret (version 5.10-13)
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
158,845
Version
5.10-13
License
GPL-2
Maintainer
Max Kuhn
Last Published
January 4th, 2012
Functions in caret (5.10-13)
Search all functions
confusionMatrix.train
Estimate a Resampled Confusion Matrix
diff.resamples
Inferential Assessments About Model Performance
nullModel
Fit a simple, non-informative model
knnreg
k-Nearest Neighbour Regression
BoxCoxTrans.default
Box-Cox Transformations
nearZeroVar
Identification of near zero variance predictors
BloodBrain
Blood Brain Barrier Data
plotClassProbs
Plot Predicted Probabilities in Classification Models
cars
Kelly Blue Book resale data for 2005 model year GM cars
modelLookup
Descriptions Of Models Available in train()
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
dummyVars
Create A Full Set of Dummy Variables
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
aucRoc
Compute the area under an ROC curve
as.table.confusionMatrix
Save Confusion Table Results
resampleSummary
Summary of resampled performance estimates
print.confusionMatrix
Print method for confusionMatrix
filterVarImp
Calculation of filter-based variable importance
print.train
Print Method for the train Class
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
predict.train
Extract predictions and class probabilities from train objects
createDataPartition
Data Splitting functions
rfe
Backwards Feature Selection
findLinearCombos
Determine linear combinations in a matrix
summary.bagEarth
Summarize a bagged earth or FDA fit
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
histogram.train
Lattice functions for plotting resampling results
confusionMatrix
Create a confusion matrix
prcomp.resamples
Principal Components Analysis of Resampling Results
caret-internal
Internal Functions
findCorrelation
Determine highly correlated variables
roc
Compute the points for an ROC curve
icr.formula
Independent Component Regression
resampleHist
Plot the resampling distribution of the model statistics
knn3
k-Nearest Neighbour Classification
pottery
Pottery from Pre-Classical Sites in Italy
panel.lift2
Lattice Panel Functions for Lift Plots
sbfControl
Control Object for Selection By Filtering (SBF)
postResample
Calculates performance across resamples
varImp
Calculation of variable importance for regression and classification models
createGrid
Tuning Parameter Grid
predictors
List predictors used in the model
trainControl
Control parameters for train
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
plot.varImp.train
Plotting variable importance measures
predict.knn3
Predictions from k-Nearest Neighbors
dotPlot
Create a dotplot of variable importance values
caretFuncs
Backwards Feature Selection Helper Functions
GermanCredit
German Credit Data
lift
Lift Plot
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
bagFDA
Bagged FDA
dhfr
Dihydrofolate Reductase Inhibitors Data
panel.needle
Needle Plot Lattice Panel
bag.default
A General Framework For Bagging
maxDissim
Maximum Dissimilarity Sampling
predict.bagEarth
Predicted values based on bagged Earth and FDA models
classDist
Compute and predict the distances to class centroids
preProcess
Pre-Processing of Predictors
resamples
Collation and Visualization of Resampling Results
rfeControl
Controlling the Feature Selection Algorithms
format.bagEarth
Format 'bagEarth' objects
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
bagEarth
Bagged Earth
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
sbf
Selection By Filtering (SBF)
pcaNNet.default
Neural Networks with a Principal Component Step
sensitivity
Calculate sensitivity, specificity and predictive values
spatialSign
Compute the multivariate spatial sign
segmentationData
Cell Body Segmentation
train
Fit Predictive Models over Different Tuning Parameters
oneSE
Selecting tuning Parameters
cox2
COX-2 Activity Data
oil
Fatty acid composition of commercial oils
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
avNNet.default
Neural Networks Using Model Averaging
tecator
Fat, Water and Protein Content of Meat Samples
plot.train
Plot Method for the train Class
caretSBF
Selection By Filtering (SBF) Helper Functions