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caret (version 5.13-037)
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
230,598
Version
5.13-037
License
GPL-2
Maintainer
Max Kuhn
Last Published
February 17th, 2012
Functions in caret (5.13-037)
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plotClassProbs
Plot Predicted Probabilities in Classification Models
lift
Lift Plot
predict.bagEarth
Predicted values based on bagged Earth and FDA models
dummyVars
Create A Full Set of Dummy Variables
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
GermanCredit
German Credit Data
as.table.confusionMatrix
Save Confusion Table Results
sbfControl
Control Object for Selection By Filtering (SBF)
knnreg
k-Nearest Neighbour Regression
cars
Kelly Blue Book resale data for 2005 model year GM cars
format.bagEarth
Format 'bagEarth' objects
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
dotPlot
Create a dotplot of variable importance values
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
sbf
Selection By Filtering (SBF)
resamples
Collation and Visualization of Resampling Results
preProcess
Pre-Processing of Predictors
pottery
Pottery from Pre-Classical Sites in Italy
histogram.train
Lattice functions for plotting resampling results
createDataPartition
Data Splitting functions
confusionMatrix.train
Estimate a Resampled Confusion Matrix
varImp
Calculation of variable importance for regression and classification models
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
predict.train
Extract predictions and class probabilities from train objects
caretSBF
Selection By Filtering (SBF) Helper Functions
predict.knn3
Predictions from k-Nearest Neighbors
maxDissim
Maximum Dissimilarity Sampling
panel.lift2
Lattice Panel Functions for Lift Plots
plot.varImp.train
Plotting variable importance measures
oneSE
Selecting tuning Parameters
pcaNNet.default
Neural Networks with a Principal Component Step
rfe
Backwards Feature Selection
train
Fit Predictive Models over Different Tuning Parameters
knn3
k-Nearest Neighbour Classification
bagEarth
Bagged Earth
panel.needle
Needle Plot Lattice Panel
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
confusionMatrix
Create a confusion matrix
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
bagFDA
Bagged FDA
filterVarImp
Calculation of filter-based variable importance
plot.train
Plot Method for the train Class
spatialSign
Compute the multivariate spatial sign
icr.formula
Independent Component Regression
segmentationData
Cell Body Segmentation
findCorrelation
Determine highly correlated variables
diff.resamples
Inferential Assessments About Model Performance
findLinearCombos
Determine linear combinations in a matrix
modelLookup
Descriptions Of Models Available in train()
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
oil
Fatty acid composition of commercial oils
resampleHist
Plot the resampling distribution of the model statistics
resampleSummary
Summary of resampled performance estimates
sensitivity
Calculate sensitivity, specificity and predictive values
BloodBrain
Blood Brain Barrier Data
classDist
Compute and predict the distances to class centroids
caret-internal
Internal Functions
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
update.train
Update and Re-fit a Model
print.train
Print Method for the train Class
print.confusionMatrix
Print method for confusionMatrix
rfeControl
Controlling the Feature Selection Algorithms
BoxCoxTrans.default
Box-Cox Transformations
cox2
COX-2 Activity Data
dhfr
Dihydrofolate Reductase Inhibitors Data
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
caretFuncs
Backwards Feature Selection Helper Functions
avNNet.default
Neural Networks Using Model Averaging
calibration
Probability Calibration Plot
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
createGrid
Tuning Parameter Grid
nearZeroVar
Identification of near zero variance predictors
nullModel
Fit a simple, non-informative model
tecator
Fat, Water and Protein Content of Meat Samples
prcomp.resamples
Principal Components Analysis of Resampling Results
predictors
List predictors used in the model
summary.bagEarth
Summarize a bagged earth or FDA fit
bag.default
A General Framework For Bagging
trainControl
Control parameters for train
postResample
Calculates performance across resamples