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caret (version 3.12)
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
3.12
License
GPL-2
Maintainer
Max Kuhn
Last Published
December 10th, 2024
Functions in caret (3.12)
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createDataPartition
Data Splitting functions
spatialSign
Compute the multivariate spatial sign
createGrid
Tuning Parameter Grid
train
Fit Predictive Models over Different Tuning Parameters
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
aucRoc
Compute the area under an ROC curve
confusionMatrix
Create a confusion matrix
cox2
COX-2 Activity Data
extractPrediction
Extract predictions and class probabilities from train objects
nearZeroVar
Identification of near zero variance predictors
BloodBrain
Blood Brain Barrier Data
findLinearCombos
Determine linear combinations in a matrix
bagFDA
Bagged FDA
predict.knn3
Predictions from k-Nearest Neighbors
sensitivity
Calculate Sensitivity, Specificity and predictive values
tecator
Fat, Water and Protein Content of Maat Samples
knn3
k-Nearest Neighbour Classification
trainControl
Control parameters for train
format.bagEarth
Format 'bagEarth' objects
findCorrelation
Determine highly correlated variables
dotPlot
Create a dotplot of variable importance values
panel.needle
Needle Plot Lattice Panel
bagEarth
Bagged Earth
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
resampleHist
Plot the resampling distribution of the model statistics
caret-internal
Internal Functions
plsda
Partial Least Squares Discriminant Analysis
oil
Fatty acid composition of commercial oils
maxDissim
Maximum Dissimilarity Sampling
plot.varImp.train
Plotting variable importance measures
filterVarImp
Calculation of filter-based variable importance
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
predict.bagEarth
Predicted values based on bagged Earth and FDA models
summary.bagEarth
Summarize a bagged earth or FDA fit
resampleSummary
Summary of resampled performance estimates
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
pottery
Pottery from Pre-Classical Sites in Italy
postResample
Calculates performance across resamples
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
applyProcessing
Data Processing on Predictor Variables (Deprecated)
roc
Compute the points for an ROC curve
varImp
Calculation of variable importance for regression and classification models
plot.train
Plot Method for the train Class
plotClassProbs
Plot Predicted Probabilities in Classification Models
preProcess
Pre-Processing of Predictors
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
print.train
Print Method for the train Class
print.confusionMatrix
Print method for confusionMatrix