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caret (version 4.18)
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
231,168
Version
4.18
License
GPL-2
Maintainer
Max Kuhn
Last Published
June 18th, 2009
Functions in caret (4.18)
Search all functions
confusionMatrix
Create a confusion matrix
filterVarImp
Calculation of filter-based variable importance
predict.train
Extract predictions and class probabilities from train objects
dotPlot
Create a dotplot of variable importance values
createGrid
Tuning Parameter Grid
nearZeroVar
Identification of near zero variance predictors
createDataPartition
Data Splitting functions
knnreg
k-Nearest Neighbour Regression
caret-internal
Internal Functions
format.bagEarth
Format 'bagEarth' objects
as.table.confusionMatrix
Save Confusion Table Results
histogram.train
Lattice functions for plotting resampling results
classDist
Compute and predict the distances to class centroids
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
applyProcessing
Data Processing on Predictor Variables (Deprecated)
BloodBrain
Blood Brain Barrier Data
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
bagEarth
Bagged Earth
oneSE
Selecting tuning Parameters
rfeControl
Controlling the Feature Selection Algorithms
knn3
k-Nearest Neighbour Classification
print.train
Print Method for the train Class
plotClassProbs
Plot Predicted Probabilities in Classification Models
print.confusionMatrix
Print method for confusionMatrix
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
preProcess
Pre-Processing of Predictors
varImp
Calculation of variable importance for regression and classification models
rfe
Backwards Feature Selection Helper Functions
train
Fit Predictive Models over Different Tuning Parameters
pcaNNet.default
Neural Networks with a Principal Component Step
predict.bagEarth
Predicted values based on bagged Earth and FDA models
findLinearCombos
Determine linear combinations in a matrix
sensitivity
Calculate sensitivity, specificity and predictive values
bagFDA
Bagged FDA
plot.train
Plot Method for the train Class
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
findCorrelation
Determine highly correlated variables
tecator
Fat, Water and Protein Content of Maat Samples
maxDissim
Maximum Dissimilarity Sampling
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
panel.needle
Needle Plot Lattice Panel
resampleHist
Plot the resampling distribution of the model statistics
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
predict.knn3
Predictions from k-Nearest Neighbors
cox2
COX-2 Activity Data
summary.bagEarth
Summarize a bagged earth or FDA fit
aucRoc
Compute the area under an ROC curve
predictors
List predictors used in the model
oil
Fatty acid composition of commercial oils
pottery
Pottery from Pre-Classical Sites in Italy
spatialSign
Compute the multivariate spatial sign
plot.varImp.train
Plotting variable importance measures
trainControl
Control parameters for train
postResample
Calculates performance across resamples
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
roc
Compute the points for an ROC curve
resampleSummary
Summary of resampled performance estimates