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caret (version 5.13-20)
Classification and Regression Training
Description
Misc functions for training and plotting classification and regression models
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7.0-1
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3.51
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3.16
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2.29
2.27
Install
install.packages('caret')
Monthly Downloads
230,598
Version
5.13-20
License
GPL-2
Maintainer
Max Kuhn
Last Published
February 6th, 2012
Functions in caret (5.13-20)
Search all functions
GermanCredit
German Credit Data
plot.train
Plot Method for the train Class
caret-internal
Internal Functions
prcomp.resamples
Principal Components Analysis of Resampling Results
bag.default
A General Framework For Bagging
as.table.confusionMatrix
Save Confusion Table Results
format.bagEarth
Format 'bagEarth' objects
createGrid
Tuning Parameter Grid
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
dotPlot
Create a dotplot of variable importance values
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
calibration
Probability Calibration Plot
BloodBrain
Blood Brain Barrier Data
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
dhfr
Dihydrofolate Reductase Inhibitors Data
BoxCoxTrans.default
Box-Cox Transformations
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
bagFDA
Bagged FDA
resampleSummary
Summary of resampled performance estimates
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
diff.resamples
Inferential Assessments About Model Performance
caretSBF
Selection By Filtering (SBF) Helper Functions
histogram.train
Lattice functions for plotting resampling results
spatialSign
Compute the multivariate spatial sign
predict.bagEarth
Predicted values based on bagged Earth and FDA models
lift
Lift Plot
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
cars
Kelly Blue Book resale data for 2005 model year GM cars
pcaNNet.default
Neural Networks with a Principal Component Step
classDist
Compute and predict the distances to class centroids
dummyVars
Create A Full Set of Dummy Variables
avNNet.default
Neural Networks Using Model Averaging
icr.formula
Independent Component Regression
update.train
Update and Re-fit a Model
predict.train
Extract predictions and class probabilities from train objects
filterVarImp
Calculation of filter-based variable importance
bagEarth
Bagged Earth
oil
Fatty acid composition of commercial oils
pottery
Pottery from Pre-Classical Sites in Italy
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
knnreg
k-Nearest Neighbour Regression
nullModel
Fit a simple, non-informative model
panel.lift2
Lattice Panel Functions for Lift Plots
rfeControl
Controlling the Feature Selection Algorithms
summary.bagEarth
Summarize a bagged earth or FDA fit
resamples
Collation and Visualization of Resampling Results
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
modelLookup
Descriptions Of Models Available in train()
print.confusionMatrix
Print method for confusionMatrix
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
createDataPartition
Data Splitting functions
cox2
COX-2 Activity Data
train
Fit Predictive Models over Different Tuning Parameters
knn3
k-Nearest Neighbour Classification
predictors
List predictors used in the model
varImp
Calculation of variable importance for regression and classification models
plot.varImp.train
Plotting variable importance measures
plotClassProbs
Plot Predicted Probabilities in Classification Models
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
findCorrelation
Determine highly correlated variables
maxDissim
Maximum Dissimilarity Sampling
postResample
Calculates performance across resamples
tecator
Fat, Water and Protein Content of Meat Samples
predict.knn3
Predictions from k-Nearest Neighbors
resampleHist
Plot the resampling distribution of the model statistics
rfe
Backwards Feature Selection
sbf
Selection By Filtering (SBF)
findLinearCombos
Determine linear combinations in a matrix
caretFuncs
Backwards Feature Selection Helper Functions
nearZeroVar
Identification of near zero variance predictors
print.train
Print Method for the train Class
oneSE
Selecting tuning Parameters
trainControl
Control parameters for train
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
panel.needle
Needle Plot Lattice Panel
preProcess
Pre-Processing of Predictors
segmentationData
Cell Body Segmentation
confusionMatrix
Create a confusion matrix
confusionMatrix.train
Estimate a Resampled Confusion Matrix
sensitivity
Calculate sensitivity, specificity and predictive values
sbfControl
Control Object for Selection By Filtering (SBF)