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caret (version 4.89)
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
4.89
License
GPL-2
Maintainer
Max Kuhn
Last Published
May 28th, 2011
Functions in caret (4.89)
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bag.default
A General Framework For Bagging
diff.resamples
Inferential Assessments About Model Performance
BloodBrain
Blood Brain Barrier Data
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
GermanCredit
German Credit Data
caret-internal
Internal Functions
as.table.confusionMatrix
Save Confusion Table Results
applyProcessing
Data Processing on Predictor Variables (Deprecated)
histogram.train
Lattice functions for plotting resampling results
classDist
Compute and predict the distances to class centroids
modelLookup
Descriptions Of Models Available in train()
knn3
k-Nearest Neighbour Classification
dotPlot
Create a dotplot of variable importance values
dhfr
Dihydrofolate Reductase Inhibitors Data
findCorrelation
Determine highly correlated variables
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
knnreg
k-Nearest Neighbour Regression
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
icr.formula
Independent Component Regression
createDataPartition
Data Splitting functions
cox2
COX-2 Activity Data
plotClassProbs
Plot Predicted Probabilities in Classification Models
confusionMatrix
Create a confusion matrix
findLinearCombos
Determine linear combinations in a matrix
pcaNNet.default
Neural Networks with a Principal Component Step
nearZeroVar
Identification of near zero variance predictors
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
predict.train
Extract predictions and class probabilities from train objects
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
predict.bagEarth
Predicted values based on bagged Earth and FDA models
preProcess
Pre-Processing of Predictors
nullModel
Fit a simple, non-informative model
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
postResample
Calculates performance across resamples
maxDissim
Maximum Dissimilarity Sampling
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
predict.knn3
Predictions from k-Nearest Neighbors
bagFDA
Bagged FDA
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
cars
Kelly Blue Book resale data for 2005 model year GM cars
panel.needle
Needle Plot Lattice Panel
caretSBF
Selection By Filtering (SBF) Helper Functions
aucRoc
Compute the area under an ROC curve
rfe
Backwards Feature Selection
plot.varImp.train
Plotting variable importance measures
varImp
Calculation of variable importance for regression and classification models
train
Fit Predictive Models over Different Tuning Parameters
sensitivity
Calculate sensitivity, specificity and predictive values
plot.train
Plot Method for the train Class
summary.bagEarth
Summarize a bagged earth or FDA fit
caretFuncs
Backwards Feature Selection Helper Functions
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
format.bagEarth
Format 'bagEarth' objects
prcomp.resamples
Principal Components Analysis of Resampling Results
resampleSummary
Summary of resampled performance estimates
print.confusionMatrix
Print method for confusionMatrix
print.train
Print Method for the train Class
resampleHist
Plot the resampling distribution of the model statistics
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
resamples
Collation and Visualization of Resampling Results
bagEarth
Bagged Earth
dummyVars
Create A Full Set of Dummy Variables
tecator
Fat, Water and Protein Content of Meat Samples
pottery
Pottery from Pre-Classical Sites in Italy
trainControl
Control parameters for train
sbf
Selection By Filtering (SBF)
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
predictors
List predictors used in the model
rfeControl
Controlling the Feature Selection Algorithms
createGrid
Tuning Parameter Grid
oneSE
Selecting tuning Parameters
filterVarImp
Calculation of filter-based variable importance
spatialSign
Compute the multivariate spatial sign
roc
Compute the points for an ROC curve
sbfControl
Control Object for Selection By Filtering (SBF)
oil
Fatty acid composition of commercial oils
BoxCoxTrans.default
Box-Cox Transformations
segmentationData
Cell Body Segmentation