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caret (version 5.07-005)
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
184,121
Version
5.07-005
License
GPL-2
Maintainer
Max Kuhn
Last Published
November 8th, 2011
Functions in caret (5.07-005)
Search functions
bagFDA
Bagged FDA
cars
Kelly Blue Book resale data for 2005 model year GM cars
caret-internal
Internal Functions
GermanCredit
German Credit Data
BoxCoxTrans.default
Box-Cox Transformations
avNNet.default
Neural Networks Using Model Averaging
bagEarth
Bagged Earth
as.table.confusionMatrix
Save Confusion Table Results
confusionMatrix.train
Estimate a Resampled Confusion Matrix
classDist
Compute and predict the distances to class centroids
confusionMatrix
Create a confusion matrix
dhfr
Dihydrofolate Reductase Inhibitors Data
cox2
COX-2 Activity Data
createGrid
Tuning Parameter Grid
diff.resamples
Inferential Assessments About Model Performance
dotPlot
Create a dotplot of variable importance values
BloodBrain
Blood Brain Barrier Data
createDataPartition
Data Splitting functions
aucRoc
Compute the area under an ROC curve
bag.default
A General Framework For Bagging
dummyVars
Create A Full Set of Dummy Variables
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
filterVarImp
Calculation of filter-based variable importance
predict.train
Extract predictions and class probabilities from train objects
icr.formula
Independent Component Regression
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
knnreg
k-Nearest Neighbour Regression
format.bagEarth
Format 'bagEarth' objects
findLinearCombos
Determine linear combinations in a matrix
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
knn3
k-Nearest Neighbour Classification
histogram.train
Lattice functions for plotting resampling results
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
maxDissim
Maximum Dissimilarity Sampling
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
oil
Fatty acid composition of commercial oils
lift
Lift Plot
pcaNNet.default
Neural Networks with a Principal Component Step
postResample
Calculates performance across resamples
modelLookup
Descriptions Of Models Available in train()
plot.varImp.train
Plotting variable importance measures
plotClassProbs
Plot Predicted Probabilities in Classification Models
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
plot.train
Plot Method for the train Class
panel.lift2
Lattice Panel Functions for Lift Plots
pottery
Pottery from Pre-Classical Sites in Italy
prcomp.resamples
Principal Components Analysis of Resampling Results
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
findCorrelation
Determine highly correlated variables
panel.needle
Needle Plot Lattice Panel
predict.bagEarth
Predicted values based on bagged Earth and FDA models
predict.knn3
Predictions from k-Nearest Neighbors
preProcess
Pre-Processing of Predictors
predictors
List predictors used in the model
resampleHist
Plot the resampling distribution of the model statistics
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
print.train
Print Method for the train Class
print.confusionMatrix
Print method for confusionMatrix
resampleSummary
Summary of resampled performance estimates
nullModel
Fit a simple, non-informative model
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
roc
Compute the points for an ROC curve
caretFuncs
Backwards Feature Selection Helper Functions
rfeControl
Controlling the Feature Selection Algorithms
rfe
Backwards Feature Selection
resamples
Collation and Visualization of Resampling Results
sbf
Selection By Filtering (SBF)
spatialSign
Compute the multivariate spatial sign
trainControl
Control parameters for train
summary.bagEarth
Summarize a bagged earth or FDA fit
nearZeroVar
Identification of near zero variance predictors
caretSBF
Selection By Filtering (SBF) Helper Functions
oneSE
Selecting tuning Parameters
sensitivity
Calculate sensitivity, specificity and predictive values
sbfControl
Control Object for Selection By Filtering (SBF)
train
Fit Predictive Models over Different Tuning Parameters
varImp
Calculation of variable importance for regression and classification models
segmentationData
Cell Body Segmentation
tecator
Fat, Water and Protein Content of Meat Samples