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caret (version 4.49)
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
4.49
License
GPL-2
Maintainer
Max Kuhn
Last Published
August 9th, 2010
Functions in caret (4.49)
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bagEarth
Bagged Earth
GermanCredit
German Credit Data
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
histogram.train
Lattice functions for plotting resampling results
knn3
k-Nearest Neighbour Classification
createDataPartition
Data Splitting functions
predict.train
Extract predictions and class probabilities from train objects
filterVarImp
Calculation of filter-based variable importance
diff.resamples
Inferential Assessments About Model Performance
nullModel
Fit a simple, non-informative model
knnreg
k-Nearest Neighbour Regression
dhfr
Dihydrofolate Reductase Inhibitors Data
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
confusionMatrix
Create a confusion matrix
bag.default
A General Framework For Bagging
rfe
Backwards Feature Selection
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
plotClassProbs
Plot Predicted Probabilities in Classification Models
findLinearCombos
Determine linear combinations in a matrix
cars
Kelly Blue Book resale data for 2005 model year GM cars
trainControl
Control parameters for train
plot.varImp.train
Plotting variable importance measures
as.table.confusionMatrix
Save Confusion Table Results
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
resamples
Collation and Visualization of Resampling Results
print.confusionMatrix
Print method for confusionMatrix
resampleHist
Plot the resampling distribution of the model statistics
sbfControl
Control Object for Selection By Filtering (SBF)
postResample
Calculates performance across resamples
oil
Fatty acid composition of commercial oils
createGrid
Tuning Parameter Grid
preProcess
Pre-Processing of Predictors
sensitivity
Calculate sensitivity, specificity and predictive values
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
resampleSummary
Summary of resampled performance estimates
findCorrelation
Determine highly correlated variables
print.train
Print Method for the train Class
pcaNNet.default
Neural Networks with a Principal Component Step
format.bagEarth
Format 'bagEarth' objects
cox2
COX-2 Activity Data
tecator
Fat, Water and Protein Content of Meat Samples
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
classDist
Compute and predict the distances to class centroids
predictors
List predictors used in the model
caretFuncs
Backwards Feature Selection Helper Functions
panel.needle
Needle Plot Lattice Panel
predict.knn3
Predictions from k-Nearest Neighbors
summary.bagEarth
Summarize a bagged earth or FDA fit
spatialSign
Compute the multivariate spatial sign
BloodBrain
Blood Brain Barrier Data
predict.bagEarth
Predicted values based on bagged Earth and FDA models
plot.train
Plot Method for the train Class
caretSBF
Selection By Filtering (SBF) Helper Functions
train
Fit Predictive Models over Different Tuning Parameters
maxDissim
Maximum Dissimilarity Sampling
oneSE
Selecting tuning Parameters
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
aucRoc
Compute the area under an ROC curve
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
rfeControl
Controlling the Feature Selection Algorithms
pottery
Pottery from Pre-Classical Sites in Italy
applyProcessing
Data Processing on Predictor Variables (Deprecated)
roc
Compute the points for an ROC curve
bagFDA
Bagged FDA
nearZeroVar
Identification of near zero variance predictors
sbf
Selection By Filtering (SBF)
varImp
Calculation of variable importance for regression and classification models
caret-internal
Internal Functions
dotPlot
Create a dotplot of variable importance values
icr.formula
Independent Component Regression
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
prcomp.resamples
Principal Components Analysis of Resampling Results