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caret (version 4.72)
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
4.72
License
GPL-2
Maintainer
Max Kuhn
Last Published
December 18th, 2010
Functions in caret (4.72)
Search all functions
diff.resamples
Inferential Assessments About Model Performance
dhfr
Dihydrofolate Reductase Inhibitors Data
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
predict.knn3
Predictions from k-Nearest Neighbors
caretFuncs
Backwards Feature Selection Helper Functions
spatialSign
Compute the multivariate spatial sign
BloodBrain
Blood Brain Barrier Data
aucRoc
Compute the area under an ROC curve
dummyVars
Create A Full Set of Dummy Variables
filterVarImp
Calculation of filter-based variable importance
maxDissim
Maximum Dissimilarity Sampling
postResample
Calculates performance across resamples
print.confusionMatrix
Print method for confusionMatrix
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
segmentationData
Cell Body Segmentation
sbfControl
Control Object for Selection By Filtering (SBF)
rfeControl
Controlling the Feature Selection Algorithms
applyProcessing
Data Processing on Predictor Variables (Deprecated)
cars
Kelly Blue Book resale data for 2005 model year GM cars
cox2
COX-2 Activity Data
createGrid
Tuning Parameter Grid
oil
Fatty acid composition of commercial oils
modelLookup
Descriptions Of Models Available in train()
panel.needle
Needle Plot Lattice Panel
resamples
Collation and Visualization of Resampling Results
nearZeroVar
Identification of near zero variance predictors
train
Fit Predictive Models over Different Tuning Parameters
bag.default
A General Framework For Bagging
predict.train
Extract predictions and class probabilities from train objects
pcaNNet.default
Neural Networks with a Principal Component Step
knn3
k-Nearest Neighbour Classification
predict.bagEarth
Predicted values based on bagged Earth and FDA models
caretSBF
Selection By Filtering (SBF) Helper Functions
tecator
Fat, Water and Protein Content of Meat Samples
rfe
Backwards Feature Selection
bagEarth
Bagged Earth
createDataPartition
Data Splitting functions
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
dotPlot
Create a dotplot of variable importance values
bagFDA
Bagged FDA
knnreg
k-Nearest Neighbour Regression
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
preProcess
Pre-Processing of Predictors
plotClassProbs
Plot Predicted Probabilities in Classification Models
GermanCredit
German Credit Data
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
findLinearCombos
Determine linear combinations in a matrix
confusionMatrix
Create a confusion matrix
oneSE
Selecting tuning Parameters
roc
Compute the points for an ROC curve
plot.train
Plot Method for the train Class
varImp
Calculation of variable importance for regression and classification models
as.table.confusionMatrix
Save Confusion Table Results
pottery
Pottery from Pre-Classical Sites in Italy
resampleSummary
Summary of resampled performance estimates
sensitivity
Calculate sensitivity, specificity and predictive values
caret-internal
Internal Functions
summary.bagEarth
Summarize a bagged earth or FDA fit
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
classDist
Compute and predict the distances to class centroids
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
plot.varImp.train
Plotting variable importance measures
print.train
Print Method for the train Class
icr.formula
Independent Component Regression
resampleHist
Plot the resampling distribution of the model statistics
trainControl
Control parameters for train
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
prcomp.resamples
Principal Components Analysis of Resampling Results
histogram.train
Lattice functions for plotting resampling results
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
nullModel
Fit a simple, non-informative model
predictors
List predictors used in the model
sbf
Selection By Filtering (SBF)
findCorrelation
Determine highly correlated variables
format.bagEarth
Format 'bagEarth' objects