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caret (version 4.76)
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
4.76
License
GPL-2
Maintainer
Max Kuhn
Last Published
January 8th, 2011
Functions in caret (4.76)
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createDataPartition
Data Splitting functions
diff.resamples
Inferential Assessments About Model Performance
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
icr.formula
Independent Component Regression
oil
Fatty acid composition of commercial oils
postResample
Calculates performance across resamples
classDist
Compute and predict the distances to class centroids
knn3
k-Nearest Neighbour Classification
bagEarth
Bagged Earth
bagFDA
Bagged FDA
maxDissim
Maximum Dissimilarity Sampling
plotClassProbs
Plot Predicted Probabilities in Classification Models
as.table.confusionMatrix
Save Confusion Table Results
filterVarImp
Calculation of filter-based variable importance
GermanCredit
German Credit Data
modelLookup
Descriptions Of Models Available in train()
applyProcessing
Data Processing on Predictor Variables (Deprecated)
caret-internal
Internal Functions
bag.default
A General Framework For Bagging
aucRoc
Compute the area under an ROC curve
BloodBrain
Blood Brain Barrier Data
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
confusionMatrix
Create a confusion matrix
histogram.train
Lattice functions for plotting resampling results
plot.varImp.train
Plotting variable importance measures
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
preProcess
Pre-Processing of Predictors
prcomp.resamples
Principal Components Analysis of Resampling Results
format.bagEarth
Format 'bagEarth' objects
plot.train
Plot Method for the train Class
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
nearZeroVar
Identification of near zero variance predictors
resampleHist
Plot the resampling distribution of the model statistics
pcaNNet.default
Neural Networks with a Principal Component Step
createGrid
Tuning Parameter Grid
print.confusionMatrix
Print method for confusionMatrix
knnreg
k-Nearest Neighbour Regression
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
findLinearCombos
Determine linear combinations in a matrix
dotPlot
Create a dotplot of variable importance values
tecator
Fat, Water and Protein Content of Meat Samples
dhfr
Dihydrofolate Reductase Inhibitors Data
varImp
Calculation of variable importance for regression and classification models
predictors
List predictors used in the model
sensitivity
Calculate sensitivity, specificity and predictive values
resamples
Collation and Visualization of Resampling Results
predict.knn3
Predictions from k-Nearest Neighbors
sbfControl
Control Object for Selection By Filtering (SBF)
sbf
Selection By Filtering (SBF)
print.train
Print Method for the train Class
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
summary.bagEarth
Summarize a bagged earth or FDA fit
pottery
Pottery from Pre-Classical Sites in Italy
cars
Kelly Blue Book resale data for 2005 model year GM cars
train
Fit Predictive Models over Different Tuning Parameters
predict.bagEarth
Predicted values based on bagged Earth and FDA models
nullModel
Fit a simple, non-informative model
oneSE
Selecting tuning Parameters
trainControl
Control parameters for train
caretSBF
Selection By Filtering (SBF) Helper Functions
spatialSign
Compute the multivariate spatial sign
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
roc
Compute the points for an ROC curve
resampleSummary
Summary of resampled performance estimates
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
caretFuncs
Backwards Feature Selection Helper Functions
panel.needle
Needle Plot Lattice Panel
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
segmentationData
Cell Body Segmentation
rfeControl
Controlling the Feature Selection Algorithms
findCorrelation
Determine highly correlated variables
rfe
Backwards Feature Selection
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
cox2
COX-2 Activity Data
dummyVars
Create A Full Set of Dummy Variables
predict.train
Extract predictions and class probabilities from train objects