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caret (version 4.73)
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
4.73
License
GPL-2
Maintainer
Max Kuhn
Last Published
December 22nd, 2010
Functions in caret (4.73)
Search all functions
classDist
Compute and predict the distances to class centroids
knn3
k-Nearest Neighbour Classification
GermanCredit
German Credit Data
bagEarth
Bagged Earth
predict.train
Extract predictions and class probabilities from train objects
createGrid
Tuning Parameter Grid
createDataPartition
Data Splitting functions
as.table.confusionMatrix
Save Confusion Table Results
caret-internal
Internal Functions
confusionMatrix
Create a confusion matrix
bagFDA
Bagged FDA
knnreg
k-Nearest Neighbour Regression
format.bagEarth
Format 'bagEarth' objects
preProcess
Pre-Processing of Predictors
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
trainControl
Control parameters for train
modelLookup
Descriptions Of Models Available in train()
resamples
Collation and Visualization of Resampling Results
filterVarImp
Calculation of filter-based variable importance
dotPlot
Create a dotplot of variable importance values
panel.needle
Needle Plot Lattice Panel
rfe
Backwards Feature Selection
cars
Kelly Blue Book resale data for 2005 model year GM cars
tecator
Fat, Water and Protein Content of Meat Samples
dhfr
Dihydrofolate Reductase Inhibitors Data
train
Fit Predictive Models over Different Tuning Parameters
nearZeroVar
Identification of near zero variance predictors
applyProcessing
Data Processing on Predictor Variables (Deprecated)
BloodBrain
Blood Brain Barrier Data
print.confusionMatrix
Print method for confusionMatrix
spatialSign
Compute the multivariate spatial sign
plotClassProbs
Plot Predicted Probabilities in Classification Models
maxDissim
Maximum Dissimilarity Sampling
dummyVars
Create A Full Set of Dummy Variables
findLinearCombos
Determine linear combinations in a matrix
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
findCorrelation
Determine highly correlated variables
diff.resamples
Inferential Assessments About Model Performance
pcaNNet.default
Neural Networks with a Principal Component Step
plot.varImp.train
Plotting variable importance measures
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
resampleHist
Plot the resampling distribution of the model statistics
pottery
Pottery from Pre-Classical Sites in Italy
resampleSummary
Summary of resampled performance estimates
plot.train
Plot Method for the train Class
predict.knn3
Predictions from k-Nearest Neighbors
caretFuncs
Backwards Feature Selection Helper Functions
print.train
Print Method for the train Class
aucRoc
Compute the area under an ROC curve
rfeControl
Controlling the Feature Selection Algorithms
varImp
Calculation of variable importance for regression and classification models
predict.bagEarth
Predicted values based on bagged Earth and FDA models
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
predictors
List predictors used in the model
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
caretSBF
Selection By Filtering (SBF) Helper Functions
oneSE
Selecting tuning Parameters
histogram.train
Lattice functions for plotting resampling results
sbfControl
Control Object for Selection By Filtering (SBF)
nullModel
Fit a simple, non-informative model
summary.bagEarth
Summarize a bagged earth or FDA fit
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
segmentationData
Cell Body Segmentation
sensitivity
Calculate sensitivity, specificity and predictive values
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
icr.formula
Independent Component Regression
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
sbf
Selection By Filtering (SBF)
oil
Fatty acid composition of commercial oils
postResample
Calculates performance across resamples
bag.default
A General Framework For Bagging
cox2
COX-2 Activity Data
prcomp.resamples
Principal Components Analysis of Resampling Results
roc
Compute the points for an ROC curve