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caret (version 4.60)
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
158,845
Version
4.60
License
GPL-2
Maintainer
Max Kuhn
Last Published
September 3rd, 2010
Functions in caret (4.60)
Search all functions
bagEarth
Bagged Earth
bagFDA
Bagged FDA
oil
Fatty acid composition of commercial oils
knn3
k-Nearest Neighbour Classification
predict.bagEarth
Predicted values based on bagged Earth and FDA models
pottery
Pottery from Pre-Classical Sites in Italy
rfeControl
Controlling the Feature Selection Algorithms
dotPlot
Create a dotplot of variable importance values
confusionMatrix
Create a confusion matrix
predict.train
Extract predictions and class probabilities from train objects
histogram.train
Lattice functions for plotting resampling results
nullModel
Fit a simple, non-informative model
plot.varImp.train
Plotting variable importance measures
createDataPartition
Data Splitting functions
resamples
Collation and Visualization of Resampling Results
caretFuncs
Backwards Feature Selection Helper Functions
caretSBF
Selection By Filtering (SBF) Helper Functions
sbfControl
Control Object for Selection By Filtering (SBF)
caret-internal
Internal Functions
knnreg
k-Nearest Neighbour Regression
print.confusionMatrix
Print method for confusionMatrix
createGrid
Tuning Parameter Grid
GermanCredit
German Credit Data
findCorrelation
Determine highly correlated variables
findLinearCombos
Determine linear combinations in a matrix
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
train
Fit Predictive Models over Different Tuning Parameters
format.bagEarth
Format 'bagEarth' objects
modelLookup
Descriptions Of Models Available in train()
nearZeroVar
Identification of near zero variance predictors
plot.train
Plot Method for the train Class
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
summary.bagEarth
Summarize a bagged earth or FDA fit
as.table.confusionMatrix
Save Confusion Table Results
diff.resamples
Inferential Assessments About Model Performance
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
oneSE
Selecting tuning Parameters
spatialSign
Compute the multivariate spatial sign
BloodBrain
Blood Brain Barrier Data
cars
Kelly Blue Book resale data for 2005 model year GM cars
dhfr
Dihydrofolate Reductase Inhibitors Data
filterVarImp
Calculation of filter-based variable importance
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
pcaNNet.default
Neural Networks with a Principal Component Step
plotClassProbs
Plot Predicted Probabilities in Classification Models
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
preProcess
Pre-Processing of Predictors
print.train
Print Method for the train Class
sensitivity
Calculate sensitivity, specificity and predictive values
trainControl
Control parameters for train
classDist
Compute and predict the distances to class centroids
applyProcessing
Data Processing on Predictor Variables (Deprecated)
icr.formula
Independent Component Regression
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
maxDissim
Maximum Dissimilarity Sampling
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
sbf
Selection By Filtering (SBF)
resampleHist
Plot the resampling distribution of the model statistics
varImp
Calculation of variable importance for regression and classification models
bag.default
A General Framework For Bagging
prcomp.resamples
Principal Components Analysis of Resampling Results
predictors
List predictors used in the model
panel.needle
Needle Plot Lattice Panel
aucRoc
Compute the area under an ROC curve
cox2
COX-2 Activity Data
postResample
Calculates performance across resamples
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
predict.knn3
Predictions from k-Nearest Neighbors
rfe
Backwards Feature Selection
resampleSummary
Summary of resampled performance estimates
roc
Compute the points for an ROC curve
tecator
Fat, Water and Protein Content of Meat Samples