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caret (version 4.27)
Classification and Regression Training
Description
Misc functions for training and plotting classification and regression models
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2.27
Install
install.packages('caret')
Monthly Downloads
230,598
Version
4.27
License
GPL-2
Maintainer
Max Kuhn
Last Published
November 5th, 2009
Functions in caret (4.27)
Search all functions
applyProcessing
Data Processing on Predictor Variables (Deprecated)
findLinearCombos
Determine linear combinations in a matrix
oil
Fatty acid composition of commercial oils
predict.bagEarth
Predicted values based on bagged Earth and FDA models
panel.needle
Needle Plot Lattice Panel
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
sensitivity
Calculate sensitivity, specificity and predictive values
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
rfe
Backwards Feature Selection
pottery
Pottery from Pre-Classical Sites in Italy
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
dotPlot
Create a dotplot of variable importance values
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
nearZeroVar
Identification of near zero variance predictors
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
plot.train
Plot Method for the train Class
resampleHist
Plot the resampling distribution of the model statistics
findCorrelation
Determine highly correlated variables
createDataPartition
Data Splitting functions
plotClassProbs
Plot Predicted Probabilities in Classification Models
knnreg
k-Nearest Neighbour Regression
BloodBrain
Blood Brain Barrier Data
histogram.train
Lattice functions for plotting resampling results
aucRoc
Compute the area under an ROC curve
maxDissim
Maximum Dissimilarity Sampling
filterVarImp
Calculation of filter-based variable importance
roc
Compute the points for an ROC curve
spatialSign
Compute the multivariate spatial sign
caret-internal
Internal Functions
cox2
COX-2 Activity Data
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
createGrid
Tuning Parameter Grid
varImp
Calculation of variable importance for regression and classification models
rfeControl
Controlling the Feature Selection Algorithms
confusionMatrix
Create a confusion matrix
classDist
Compute and predict the distances to class centroids
predict.train
Extract predictions and class probabilities from train objects
postResample
Calculates performance across resamples
preProcess
Pre-Processing of Predictors
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
print.confusionMatrix
Print method for confusionMatrix
trainControl
Control parameters for train
bagFDA
Bagged FDA
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
predictors
List predictors used in the model
oneSE
Selecting tuning Parameters
tecator
Fat, Water and Protein Content of Maat Samples
resampleSummary
Summary of resampled performance estimates
summary.bagEarth
Summarize a bagged earth or FDA fit
plot.varImp.train
Plotting variable importance measures
caretFuncs
Backwards Feature Selection Helper Functions
knn3
k-Nearest Neighbour Classification
train
Fit Predictive Models over Different Tuning Parameters
bagEarth
Bagged Earth
format.bagEarth
Format 'bagEarth' objects
as.table.confusionMatrix
Save Confusion Table Results
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
pcaNNet.default
Neural Networks with a Principal Component Step
predict.knn3
Predictions from k-Nearest Neighbors
print.train
Print Method for the train Class