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caret (version 5.16-24)
Classification and Regression Training
Description
Misc functions for training and plotting classification and regression models
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3.51
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Install
install.packages('caret')
Monthly Downloads
230,598
Version
5.16-24
License
GPL-2
Maintainer
Max Kuhn
Last Published
June 20th, 2013
Functions in caret (5.16-24)
Search all functions
pottery
Pottery from Pre-Classical Sites in Italy
spatialSign
Compute the multivariate spatial sign
createDataPartition
Data Splitting functions
diff.resamples
Inferential Assessments About Model Performance
knn3
k-Nearest Neighbour Classification
filterVarImp
Calculation of filter-based variable importance
BoxCoxTrans.default
Box-Cox Transformations
dotPlot
Create a dotplot of variable importance values
as.table.confusionMatrix
Save Confusion Table Results
GermanCredit
German Credit Data
dhfr
Dihydrofolate Reductase Inhibitors Data
resampleSummary
Summary of resampled performance estimates
confusionMatrix.train
Estimate a Resampled Confusion Matrix
plotClassProbs
Plot Predicted Probabilities in Classification Models
pcaNNet.default
Neural Networks with a Principal Component Step
predict.knn3
Predictions from k-Nearest Neighbors
classDist
Compute and predict the distances to class centroids
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
findCorrelation
Determine highly correlated variables
nullModel
Fit a simple, non-informative model
modelLookup
Descriptions Of Models Available in train()
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
confusionMatrix
Create a confusion matrix
predictors
List predictors used in the model
avNNet.default
Neural Networks Using Model Averaging
rfe
Backwards Feature Selection
bagEarth
Bagged Earth
cox2
COX-2 Activity Data
oil
Fatty acid composition of commercial oils
tecator
Fat, Water and Protein Content of Meat Samples
icr.formula
Independent Component Regression
findLinearCombos
Determine linear combinations in a matrix
caretFuncs
Backwards Feature Selection Helper Functions
caretSBF
Selection By Filtering (SBF) Helper Functions
format.bagEarth
Format 'bagEarth' objects
print.confusionMatrix
Print method for confusionMatrix
segmentationData
Cell Body Segmentation
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
resamples
Collation and Visualization of Resampling Results
bag.default
A General Framework For Bagging
knnreg
k-Nearest Neighbour Regression
sensitivity
Calculate sensitivity, specificity and predictive values
caret-internal
Internal Functions
postResample
Calculates performance across resamples
rfeControl
Controlling the Feature Selection Algorithms
twoClassSim
Two-Class Simulations
predict.train
Extract predictions and class probabilities from train objects
panel.needle
Needle Plot Lattice Panel
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
maxDissim
Maximum Dissimilarity Sampling
BloodBrain
Blood Brain Barrier Data
histogram.train
Lattice functions for plotting resampling results
update.train
Update and Re-fit a Model
bagFDA
Bagged FDA
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
varImp
Calculation of variable importance for regression and classification models
plot.train
Plot Method for the train Class
predict.bagEarth
Predicted values based on bagged Earth and FDA models
cars
Kelly Blue Book resale data for 2005 model year GM cars
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
plot.varImp.train
Plotting variable importance measures
panel.lift2
Lattice Panel Functions for Lift Plots
calibration
Probability Calibration Plot
preProcess
Pre-Processing of Predictors
oneSE
Selecting tuning Parameters
sbf
Selection By Filtering (SBF)
summary.bagEarth
Summarize a bagged earth or FDA fit
dummyVars
Create A Full Set of Dummy Variables
prcomp.resamples
Principal Components Analysis of Resampling Results
sbfControl
Control Object for Selection By Filtering (SBF)
lift
Lift Plot
createGrid
Tuning Parameter Grid
trainControl
Control parameters for train
nearZeroVar
Identification of near zero variance predictors
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
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
downSample
Down- and Up-Sampling Imbalanced Data
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
train
Fit Predictive Models over Different Tuning Parameters