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caret (version 5.15-61)
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
5.15-61
License
GPL-2
Maintainer
Max Kuhn
Last Published
February 12th, 2013
Functions in caret (5.15-61)
Search all functions
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
nullModel
Fit a simple, non-informative model
print.train
Print Method for the train Class
caretSBF
Selection By Filtering (SBF) Helper Functions
rfe
Backwards Feature Selection
bag.default
A General Framework For Bagging
downSample
Down- and Up-Sampling Imbalanced Data
as.table.confusionMatrix
Save Confusion Table Results
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
histogram.train
Lattice functions for plotting resampling results
predict.train
Extract predictions and class probabilities from train objects
print.confusionMatrix
Print method for confusionMatrix
postResample
Calculates performance across resamples
panel.needle
Needle Plot Lattice Panel
nearZeroVar
Identification of near zero variance predictors
bagFDA
Bagged FDA
bagEarth
Bagged Earth
plot.train
Plot Method for the train Class
icr.formula
Independent Component Regression
dummyVars
Create A Full Set of Dummy Variables
cox2
COX-2 Activity Data
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
resamples
Collation and Visualization of Resampling Results
findLinearCombos
Determine linear combinations in a matrix
dotPlot
Create a dotplot of variable importance values
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
predict.bagEarth
Predicted values based on bagged Earth and FDA models
segmentationData
Cell Body Segmentation
BloodBrain
Blood Brain Barrier Data
format.bagEarth
Format 'bagEarth' objects
predictors
List predictors used in the model
caretFuncs
Backwards Feature Selection Helper Functions
classDist
Compute and predict the distances to class centroids
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
rfeControl
Controlling the Feature Selection Algorithms
trainControl
Control parameters for train
preProcess
Pre-Processing of Predictors
cars
Kelly Blue Book resale data for 2005 model year GM cars
maxDissim
Maximum Dissimilarity Sampling
BoxCoxTrans.default
Box-Cox Transformations
modelLookup
Descriptions Of Models Available in train()
avNNet.default
Neural Networks Using Model Averaging
prcomp.resamples
Principal Components Analysis of Resampling Results
caret-internal
Internal Functions
createDataPartition
Data Splitting functions
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
confusionMatrix
Create a confusion matrix
GermanCredit
German Credit Data
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
pcaNNet.default
Neural Networks with a Principal Component Step
update.train
Update and Re-fit a Model
resampleHist
Plot the resampling distribution of the model statistics
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
findCorrelation
Determine highly correlated variables
knn3
k-Nearest Neighbour Classification
dhfr
Dihydrofolate Reductase Inhibitors Data
summary.bagEarth
Summarize a bagged earth or FDA fit
knnreg
k-Nearest Neighbour Regression
oil
Fatty acid composition of commercial oils
tecator
Fat, Water and Protein Content of Meat Samples
filterVarImp
Calculation of filter-based variable importance
oneSE
Selecting tuning Parameters
sensitivity
Calculate sensitivity, specificity and predictive values
plot.varImp.train
Plotting variable importance measures
createGrid
Tuning Parameter Grid
plotClassProbs
Plot Predicted Probabilities in Classification Models
resampleSummary
Summary of resampled performance estimates
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
pottery
Pottery from Pre-Classical Sites in Italy
sbf
Selection By Filtering (SBF)
varImp
Calculation of variable importance for regression and classification models
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
predict.knn3
Predictions from k-Nearest Neighbors
sbfControl
Control Object for Selection By Filtering (SBF)
spatialSign
Compute the multivariate spatial sign
calibration
Probability Calibration Plot
diff.resamples
Inferential Assessments About Model Performance
confusionMatrix.train
Estimate a Resampled Confusion Matrix
lift
Lift Plot
panel.lift2
Lattice Panel Functions for Lift Plots
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data