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caret (version 5.09-012)
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
5.09-012
License
GPL-2
Maintainer
Max Kuhn
Last Published
December 11th, 2011
Functions in caret (5.09-012)
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dummyVars
Create A Full Set of Dummy Variables
filterVarImp
Calculation of filter-based variable importance
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
BloodBrain
Blood Brain Barrier Data
BoxCoxTrans.default
Box-Cox Transformations
avNNet.default
Neural Networks Using Model Averaging
as.table.confusionMatrix
Save Confusion Table Results
predictors
List predictors used in the model
bagEarth
Bagged Earth
spatialSign
Compute the multivariate spatial sign
confusionMatrix
Create a confusion matrix
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
nearZeroVar
Identification of near zero variance predictors
findLinearCombos
Determine linear combinations in a matrix
cox2
COX-2 Activity Data
lift
Lift Plot
segmentationData
Cell Body Segmentation
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
cars
Kelly Blue Book resale data for 2005 model year GM cars
classDist
Compute and predict the distances to class centroids
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
createGrid
Tuning Parameter Grid
predict.knn3
Predictions from k-Nearest Neighbors
nullModel
Fit a simple, non-informative model
panel.needle
Needle Plot Lattice Panel
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
rfe
Backwards Feature Selection
plotClassProbs
Plot Predicted Probabilities in Classification Models
resamples
Collation and Visualization of Resampling Results
maxDissim
Maximum Dissimilarity Sampling
roc
Compute the points for an ROC curve
pottery
Pottery from Pre-Classical Sites in Italy
icr.formula
Independent Component Regression
oneSE
Selecting tuning Parameters
summary.bagEarth
Summarize a bagged earth or FDA fit
trainControl
Control parameters for train
aucRoc
Compute the area under an ROC curve
caretSBF
Selection By Filtering (SBF) Helper Functions
predict.bagEarth
Predicted values based on bagged Earth and FDA models
bagFDA
Bagged FDA
dotPlot
Create a dotplot of variable importance values
tecator
Fat, Water and Protein Content of Meat Samples
print.train
Print Method for the train Class
bag.default
A General Framework For Bagging
postResample
Calculates performance across resamples
oil
Fatty acid composition of commercial oils
plot.varImp.train
Plotting variable importance measures
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
modelLookup
Descriptions Of Models Available in train()
panel.lift2
Lattice Panel Functions for Lift Plots
predict.train
Extract predictions and class probabilities from train objects
createDataPartition
Data Splitting functions
resampleHist
Plot the resampling distribution of the model statistics
knn3
k-Nearest Neighbour Classification
GermanCredit
German Credit Data
histogram.train
Lattice functions for plotting resampling results
preProcess
Pre-Processing of Predictors
format.bagEarth
Format 'bagEarth' objects
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
diff.resamples
Inferential Assessments About Model Performance
varImp
Calculation of variable importance for regression and classification models
train
Fit Predictive Models over Different Tuning Parameters
pcaNNet.default
Neural Networks with a Principal Component Step
findCorrelation
Determine highly correlated variables
resampleSummary
Summary of resampled performance estimates
rfeControl
Controlling the Feature Selection Algorithms
caretFuncs
Backwards Feature Selection Helper Functions
print.confusionMatrix
Print method for confusionMatrix
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
sbf
Selection By Filtering (SBF)
plot.train
Plot Method for the train Class
caret-internal
Internal Functions
confusionMatrix.train
Estimate a Resampled Confusion Matrix
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
dhfr
Dihydrofolate Reductase Inhibitors Data
knnreg
k-Nearest Neighbour Regression
prcomp.resamples
Principal Components Analysis of Resampling Results
sensitivity
Calculate sensitivity, specificity and predictive values
sbfControl
Control Object for Selection By Filtering (SBF)