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caret (version 4.12)
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
230,598
Version
4.12
License
GPL-2
Maintainer
Max Kuhn
Last Published
May 8th, 2009
Functions in caret (4.12)
Search all functions
as.table.confusionMatrix
Save Confusion Table Results
BloodBrain
Blood Brain Barrier Data
aucRoc
Compute the area under an ROC curve
confusionMatrix
Create a confusion matrix
createDataPartition
Data Splitting functions
format.bagEarth
Format 'bagEarth' objects
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
cox2
COX-2 Activity Data
createGrid
Tuning Parameter Grid
applyProcessing
Data Processing on Predictor Variables (Deprecated)
pcaNNet.default
Neural Networks with a Principal Component Step
findLinearCombos
Determine linear combinations in a matrix
caret-internal
Internal Functions
maxDissim
Maximum Dissimilarity Sampling
bagEarth
Bagged Earth
oil
Fatty acid composition of commercial oils
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
filterVarImp
Calculation of filter-based variable importance
panel.needle
Needle Plot Lattice Panel
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
dotPlot
Create a dotplot of variable importance values
knn3
k-Nearest Neighbour Classification
predict.train
Extract predictions and class probabilities from train objects
histogram.train
Lattice functions for plotting resampling results
roc
Compute the points for an ROC curve
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
plot.train
Plot Method for the train Class
rfeControl
Controlling the Feature Selection Algorithms
classDist
Compute and predict the distances to class centroids
bagFDA
Bagged FDA
spatialSign
Compute the multivariate spatial sign
plotClassProbs
Plot Predicted Probabilities in Classification Models
rfe
Backwards Feature Selection
findCorrelation
Determine highly correlated variables
oneSE
Selecting tuning Parameters
resampleHist
Plot the resampling distribution of the model statistics
postResample
Calculates performance across resamples
trainControl
Control parameters for train
resampleSummary
Summary of resampled performance estimates
predict.bagEarth
Predicted values based on bagged Earth and FDA models
pottery
Pottery from Pre-Classical Sites in Italy
plot.varImp.train
Plotting variable importance measures
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
tecator
Fat, Water and Protein Content of Maat Samples
summary.bagEarth
Summarize a bagged earth or FDA fit
sensitivity
Calculate sensitivity, specificity and predictive values
predictors
List predictors used in the model
predict.knn3
Predictions from k-Nearest Neighbors
preProcess
Pre-Processing of Predictors
print.confusionMatrix
Print method for confusionMatrix
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
train
Fit Predictive Models over Different Tuning Parameters
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
print.train
Print Method for the train Class
nearZeroVar
Identification of near zero variance predictors
knnreg
k-Nearest Neighbour Regression
varImp
Calculation of variable importance for regression and classification models