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caret (version 5.15-044)
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
5.15-044
License
GPL-2
Maintainer
Max Kuhn
Last Published
October 3rd, 2012
Functions in caret (5.15-044)
Search all functions
createDataPartition
Data Splitting functions
predict.train
Extract predictions and class probabilities from train objects
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
BloodBrain
Blood Brain Barrier Data
confusionMatrix.train
Estimate a Resampled Confusion Matrix
findLinearCombos
Determine linear combinations in a matrix
dhfr
Dihydrofolate Reductase Inhibitors Data
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
nullModel
Fit a simple, non-informative model
plot.varImp.train
Plotting variable importance measures
findCorrelation
Determine highly correlated variables
bagEarth
Bagged Earth
diff.resamples
Inferential Assessments About Model Performance
caret-internal
Internal Functions
filterVarImp
Calculation of filter-based variable importance
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
avNNet.default
Neural Networks Using Model Averaging
classDist
Compute and predict the distances to class centroids
icr.formula
Independent Component Regression
knnreg
k-Nearest Neighbour Regression
cars
Kelly Blue Book resale data for 2005 model year GM cars
format.bagEarth
Format 'bagEarth' objects
BoxCoxTrans.default
Box-Cox Transformations
createGrid
Tuning Parameter Grid
calibration
Probability Calibration Plot
bag.default
A General Framework For Bagging
oil
Fatty acid composition of commercial oils
dotPlot
Create a dotplot of variable importance values
pcaNNet.default
Neural Networks with a Principal Component Step
histogram.train
Lattice functions for plotting resampling results
downSample
Down- and Up-Sampling Imbalanced Data
cox2
COX-2 Activity Data
maxDissim
Maximum Dissimilarity Sampling
lift
Lift Plot
panel.lift2
Lattice Panel Functions for Lift Plots
panel.needle
Needle Plot Lattice Panel
resampleHist
Plot the resampling distribution of the model statistics
print.train
Print Method for the train Class
resampleSummary
Summary of resampled performance estimates
as.table.confusionMatrix
Save Confusion Table Results
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
prcomp.resamples
Principal Components Analysis of Resampling Results
segmentationData
Cell Body Segmentation
sensitivity
Calculate sensitivity, specificity and predictive values
plot.train
Plot Method for the train Class
caretFuncs
Backwards Feature Selection Helper Functions
sbf
Selection By Filtering (SBF)
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
predictors
List predictors used in the model
rfe
Backwards Feature Selection
plotClassProbs
Plot Predicted Probabilities in Classification Models
rfeControl
Controlling the Feature Selection Algorithms
predict.knn3
Predictions from k-Nearest Neighbors
sbfControl
Control Object for Selection By Filtering (SBF)
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
update.train
Update and Re-fit a Model
varImp
Calculation of variable importance for regression and classification models
preProcess
Pre-Processing of Predictors
pottery
Pottery from Pre-Classical Sites in Italy
caretSBF
Selection By Filtering (SBF) Helper Functions
summary.bagEarth
Summarize a bagged earth or FDA fit
trainControl
Control parameters for train
tecator
Fat, Water and Protein Content of Meat Samples
postResample
Calculates performance across resamples
resamples
Collation and Visualization of Resampling Results
predict.bagEarth
Predicted values based on bagged Earth and FDA models
GermanCredit
German Credit Data
spatialSign
Compute the multivariate spatial sign
modelLookup
Descriptions Of Models Available in train()
knn3
k-Nearest Neighbour Classification
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
nearZeroVar
Identification of near zero variance predictors
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
confusionMatrix
Create a confusion matrix
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
print.confusionMatrix
Print method for confusionMatrix
bagFDA
Bagged FDA
oneSE
Selecting tuning Parameters