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caret (version 5.03-003)
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
163,965
Version
5.03-003
License
GPL-2
Maintainer
Max Kuhn
Last Published
September 24th, 2011
Functions in caret (5.03-003)
Search all functions
GermanCredit
German Credit Data
avNNet.default
Neural Networks Using Model Averaging
knn3
k-Nearest Neighbour Classification
cars
Kelly Blue Book resale data for 2005 model year GM cars
bag.default
A General Framework For Bagging
classDist
Compute and predict the distances to class centroids
bagEarth
Bagged Earth
nullModel
Fit a simple, non-informative model
bagFDA
Bagged FDA
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
plot.train
Plot Method for the train Class
lift
Lift Plot
diff.resamples
Inferential Assessments About Model Performance
cox2
COX-2 Activity Data
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
maxDissim
Maximum Dissimilarity Sampling
caret-internal
Internal Functions
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
plotClassProbs
Plot Predicted Probabilities in Classification Models
print.confusionMatrix
Print method for confusionMatrix
oil
Fatty acid composition of commercial oils
confusionMatrix
Create a confusion matrix
dummyVars
Create A Full Set of Dummy Variables
postResample
Calculates performance across resamples
pcaNNet.default
Neural Networks with a Principal Component Step
predict.bagEarth
Predicted values based on bagged Earth and FDA models
dhfr
Dihydrofolate Reductase Inhibitors Data
predict.train
Extract predictions and class probabilities from train objects
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
nearZeroVar
Identification of near zero variance predictors
histogram.train
Lattice functions for plotting resampling results
as.table.confusionMatrix
Save Confusion Table Results
format.bagEarth
Format 'bagEarth' objects
preProcess
Pre-Processing of Predictors
plot.varImp.train
Plotting variable importance measures
BloodBrain
Blood Brain Barrier Data
prcomp.resamples
Principal Components Analysis of Resampling Results
aucRoc
Compute the area under an ROC curve
icr.formula
Independent Component Regression
BoxCoxTrans.default
Box-Cox Transformations
findCorrelation
Determine highly correlated variables
confusionMatrix.train
Estimate a Resampled Confusion Matrix
modelLookup
Descriptions Of Models Available in train()
filterVarImp
Calculation of filter-based variable importance
knnreg
k-Nearest Neighbour Regression
train
Fit Predictive Models over Different Tuning Parameters
predict.knn3
Predictions from k-Nearest Neighbors
panel.needle
Needle Plot Lattice Panel
resamples
Collation and Visualization of Resampling Results
createGrid
Tuning Parameter Grid
findLinearCombos
Determine linear combinations in a matrix
sbf
Selection By Filtering (SBF)
resampleHist
Plot the resampling distribution of the model statistics
caretFuncs
Backwards Feature Selection Helper Functions
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
varImp
Calculation of variable importance for regression and classification models
segmentationData
Cell Body Segmentation
tecator
Fat, Water and Protein Content of Meat Samples
caretSBF
Selection By Filtering (SBF) Helper Functions
summary.bagEarth
Summarize a bagged earth or FDA fit
oneSE
Selecting tuning Parameters
spatialSign
Compute the multivariate spatial sign
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
trainControl
Control parameters for train
predictors
List predictors used in the model
resampleSummary
Summary of resampled performance estimates
rfeControl
Controlling the Feature Selection Algorithms
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
print.train
Print Method for the train Class
pottery
Pottery from Pre-Classical Sites in Italy
roc
Compute the points for an ROC curve
createDataPartition
Data Splitting functions
dotPlot
Create a dotplot of variable importance values
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
sbfControl
Control Object for Selection By Filtering (SBF)
rfe
Backwards Feature Selection
sensitivity
Calculate sensitivity, specificity and predictive values