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caret (version 4.99)
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
143,228
Version
4.99
License
GPL-2
Maintainer
Max Kuhn
Last Published
September 2nd, 2011
Functions in caret (4.99)
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aucRoc
Compute the area under an ROC curve
filterVarImp
Calculation of filter-based variable importance
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
diff.resamples
Inferential Assessments About Model Performance
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
resampleHist
Plot the resampling distribution of the model statistics
cox2
COX-2 Activity Data
dhfr
Dihydrofolate Reductase Inhibitors Data
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
resamples
Collation and Visualization of Resampling Results
icr.formula
Independent Component Regression
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
confusionMatrix
Create a confusion matrix
nearZeroVar
Identification of near zero variance predictors
predict.bagEarth
Predicted values based on bagged Earth and FDA models
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
knn3
k-Nearest Neighbour Classification
postResample
Calculates performance across resamples
predictors
List predictors used in the model
pottery
Pottery from Pre-Classical Sites in Italy
plotClassProbs
Plot Predicted Probabilities in Classification Models
rfeControl
Controlling the Feature Selection Algorithms
sensitivity
Calculate sensitivity, specificity and predictive values
oneSE
Selecting tuning Parameters
print.train
Print Method for the train Class
findCorrelation
Determine highly correlated variables
BloodBrain
Blood Brain Barrier Data
modelLookup
Descriptions Of Models Available in train()
sbfControl
Control Object for Selection By Filtering (SBF)
caret-internal
Internal Functions
knnreg
k-Nearest Neighbour Regression
maxDissim
Maximum Dissimilarity Sampling
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
BoxCoxTrans.default
Box-Cox Transformations
createGrid
Tuning Parameter Grid
trainControl
Control parameters for train
findLinearCombos
Determine linear combinations in a matrix
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
dotPlot
Create a dotplot of variable importance values
varImp
Calculation of variable importance for regression and classification models
print.confusionMatrix
Print method for confusionMatrix
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
avNNet.default
Neural Networks Using Model Averaging
caretSBF
Selection By Filtering (SBF) Helper Functions
preProcess
Pre-Processing of Predictors
GermanCredit
German Credit Data
tecator
Fat, Water and Protein Content of Meat Samples
createDataPartition
Data Splitting functions
plot.train
Plot Method for the train Class
cars
Kelly Blue Book resale data for 2005 model year GM cars
bag.default
A General Framework For Bagging
histogram.train
Lattice functions for plotting resampling results
caretFuncs
Backwards Feature Selection Helper Functions
sbf
Selection By Filtering (SBF)
prcomp.resamples
Principal Components Analysis of Resampling Results
panel.needle
Needle Plot Lattice Panel
classDist
Compute and predict the distances to class centroids
as.table.confusionMatrix
Save Confusion Table Results
format.bagEarth
Format 'bagEarth' objects
oil
Fatty acid composition of commercial oils
nullModel
Fit a simple, non-informative model
plot.varImp.train
Plotting variable importance measures
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
resampleSummary
Summary of resampled performance estimates
bagFDA
Bagged FDA
rfe
Backwards Feature Selection
segmentationData
Cell Body Segmentation
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
pcaNNet.default
Neural Networks with a Principal Component Step
dummyVars
Create A Full Set of Dummy Variables
spatialSign
Compute the multivariate spatial sign
bagEarth
Bagged Earth
predict.train
Extract predictions and class probabilities from train objects
confusionMatrix.train
Estimate a Resampled Confusion Matrix
predict.knn3
Predictions from k-Nearest Neighbors
roc
Compute the points for an ROC curve
summary.bagEarth
Summarize a bagged earth or FDA fit
train
Fit Predictive Models over Different Tuning Parameters