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caret (version 4.85)
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
138,220
Version
4.85
License
GPL-2
Maintainer
Max Kuhn
Last Published
April 3rd, 2011
Functions in caret (4.85)
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cars
Kelly Blue Book resale data for 2005 model year GM cars
rfe
Backwards Feature Selection
plotClassProbs
Plot Predicted Probabilities in Classification Models
dummyVars
Create A Full Set of Dummy Variables
sbfControl
Control Object for Selection By Filtering (SBF)
findLinearCombos
Determine linear combinations in a matrix
diff.resamples
Inferential Assessments About Model Performance
aucRoc
Compute the area under an ROC curve
tecator
Fat, Water and Protein Content of Meat Samples
applyProcessing
Data Processing on Predictor Variables (Deprecated)
dotPlot
Create a dotplot of variable importance values
bag.default
A General Framework For Bagging
knn3
k-Nearest Neighbour Classification
confusionMatrix
Create a confusion matrix
classDist
Compute and predict the distances to class centroids
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
caretFuncs
Backwards Feature Selection Helper Functions
GermanCredit
German Credit Data
nearZeroVar
Identification of near zero variance predictors
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
filterVarImp
Calculation of filter-based variable importance
panel.needle
Needle Plot Lattice Panel
bagFDA
Bagged FDA
createGrid
Tuning Parameter Grid
spatialSign
Compute the multivariate spatial sign
knnreg
k-Nearest Neighbour Regression
sensitivity
Calculate sensitivity, specificity and predictive values
nullModel
Fit a simple, non-informative model
postResample
Calculates performance across resamples
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
pcaNNet.default
Neural Networks with a Principal Component Step
train
Fit Predictive Models over Different Tuning Parameters
predict.knn3
Predictions from k-Nearest Neighbors
preProcess
Pre-Processing of Predictors
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
roc
Compute the points for an ROC curve
BloodBrain
Blood Brain Barrier Data
summary.bagEarth
Summarize a bagged earth or FDA fit
as.table.confusionMatrix
Save Confusion Table Results
plot.varImp.train
Plotting variable importance measures
rfeControl
Controlling the Feature Selection Algorithms
prcomp.resamples
Principal Components Analysis of Resampling Results
sbf
Selection By Filtering (SBF)
print.train
Print Method for the train Class
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
oneSE
Selecting tuning Parameters
predictors
List predictors used in the model
plot.train
Plot Method for the train Class
BoxCoxTrans.default
Box-Cox Transformations
oil
Fatty acid composition of commercial oils
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
dhfr
Dihydrofolate Reductase Inhibitors Data
icr.formula
Independent Component Regression
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
resampleSummary
Summary of resampled performance estimates
resampleHist
Plot the resampling distribution of the model statistics
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
createDataPartition
Data Splitting functions
modelLookup
Descriptions Of Models Available in train()
caretSBF
Selection By Filtering (SBF) Helper Functions
maxDissim
Maximum Dissimilarity Sampling
caret-internal
Internal Functions
trainControl
Control parameters for train
pottery
Pottery from Pre-Classical Sites in Italy
histogram.train
Lattice functions for plotting resampling results
varImp
Calculation of variable importance for regression and classification models
predict.bagEarth
Predicted values based on bagged Earth and FDA models
findCorrelation
Determine highly correlated variables
predict.train
Extract predictions and class probabilities from train objects
print.confusionMatrix
Print method for confusionMatrix
segmentationData
Cell Body Segmentation
resamples
Collation and Visualization of Resampling Results
format.bagEarth
Format 'bagEarth' objects
bagEarth
Bagged Earth
cox2
COX-2 Activity Data