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caret (version 4.64)
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
160,352
Version
4.64
License
GPL-2
Maintainer
Max Kuhn
Last Published
October 7th, 2010
Functions in caret (4.64)
Search functions
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
findLinearCombos
Determine linear combinations in a matrix
plot.train
Plot Method for the train Class
plotClassProbs
Plot Predicted Probabilities in Classification Models
postResample
Calculates performance across resamples
spatialSign
Compute the multivariate spatial sign
oneSE
Selecting tuning Parameters
resampleHist
Plot the resampling distribution of the model statistics
bag.default
A General Framework For Bagging
caret-internal
Internal Functions
diff.resamples
Inferential Assessments About Model Performance
icr.formula
Independent Component Regression
resampleSummary
Summary of resampled performance estimates
bagFDA
Bagged FDA
resamples
Collation and Visualization of Resampling Results
classDist
Compute and predict the distances to class centroids
knn3
k-Nearest Neighbour Classification
maxDissim
Maximum Dissimilarity Sampling
modelLookup
Descriptions Of Models Available in train()
nearZeroVar
Identification of near zero variance predictors
print.confusionMatrix
Print method for confusionMatrix
roc
Compute the points for an ROC curve
sbfControl
Control Object for Selection By Filtering (SBF)
caretSBF
Selection By Filtering (SBF) Helper Functions
GermanCredit
German Credit Data
cars
Kelly Blue Book resale data for 2005 model year GM cars
histogram.train
Lattice functions for plotting resampling results
dotPlot
Create a dotplot of variable importance values
createDataPartition
Data Splitting functions
predictors
List predictors used in the model
filterVarImp
Calculation of filter-based variable importance
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
knnreg
k-Nearest Neighbour Regression
predict.bagEarth
Predicted values based on bagged Earth and FDA models
panel.needle
Needle Plot Lattice Panel
as.table.confusionMatrix
Save Confusion Table Results
BloodBrain
Blood Brain Barrier Data
sensitivity
Calculate sensitivity, specificity and predictive values
aucRoc
Compute the area under an ROC curve
dhfr
Dihydrofolate Reductase Inhibitors Data
format.bagEarth
Format 'bagEarth' objects
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
rfeControl
Controlling the Feature Selection Algorithms
varImp
Calculation of variable importance for regression and classification models
prcomp.resamples
Principal Components Analysis of Resampling Results
summary.bagEarth
Summarize a bagged earth or FDA fit
sbf
Selection By Filtering (SBF)
caretFuncs
Backwards Feature Selection Helper Functions
trainControl
Control parameters for train
tecator
Fat, Water and Protein Content of Meat Samples
applyProcessing
Data Processing on Predictor Variables (Deprecated)
cox2
COX-2 Activity Data
confusionMatrix
Create a confusion matrix
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
nullModel
Fit a simple, non-informative model
oil
Fatty acid composition of commercial oils
plot.varImp.train
Plotting variable importance measures
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
pcaNNet.default
Neural Networks with a Principal Component Step
findCorrelation
Determine highly correlated variables
pottery
Pottery from Pre-Classical Sites in Italy
rfe
Backwards Feature Selection
bagEarth
Bagged Earth
predict.knn3
Predictions from k-Nearest Neighbors
print.train
Print Method for the train Class
predict.train
Extract predictions and class probabilities from train objects
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
createGrid
Tuning Parameter Grid
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
preProcess
Pre-Processing of Predictors
train
Fit Predictive Models over Different Tuning Parameters