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caret (version 6.0-30)
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
138,220
Version
6.0-30
License
GPL-2
Maintainer
Max Kuhn
Last Published
June 4th, 2014
Functions in caret (6.0-30)
Search all functions
format.bagEarth
Format 'bagEarth' objects
cars
Kelly Blue Book resale data for 2005 model year GM cars
bagEarth
Bagged Earth
findCorrelation
Determine highly correlated variables
confusionMatrix.train
Estimate a Resampled Confusion Matrix
icr.formula
Independent Component Regression
modelLookup
Tools for Models Available in
train
dotPlot
Create a dotplot of variable importance values
panel.lift2
Lattice Panel Functions for Lift Plots
avNNet.default
Neural Networks Using Model Averaging
bag.default
A General Framework For Bagging
nullModel
Fit a simple, non-informative model
preProcess
Pre-Processing of Predictors
classDist
Compute and predict the distances to class centroids
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
dhfr
Dihydrofolate Reductase Inhibitors Data
createDataPartition
Data Splitting functions
GermanCredit
German Credit Data
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
resampleHist
Plot the resampling distribution of the model statistics
diff.resamples
Inferential Assessments About Model Performance
filterVarImp
Calculation of filter-based variable importance
caretFuncs
Backwards Feature Selection Helper Functions
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
oil
Fatty acid composition of commercial oils
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
calibration
Probability Calibration Plot
knnreg
k-Nearest Neighbour Regression
nearZeroVar
Identification of near zero variance predictors
trainControl
Control parameters for train
plot.train
Plot Method for the train Class
dummyVars
Create A Full Set of Dummy Variables
findLinearCombos
Determine linear combinations in a matrix
predict.bagEarth
Predicted values based on bagged Earth and FDA models
plotClassProbs
Plot Predicted Probabilities in Classification Models
twoClassSim
Two-Class Simulations
BloodBrain
Blood Brain Barrier Data
resampleSummary
Summary of resampled performance estimates
predict.train
Extract predictions and class probabilities from train objects
resamples
Collation and Visualization of Resampling Results
rfeControl
Controlling the Feature Selection Algorithms
histogram.train
Lattice functions for plotting resampling results
pcaNNet.default
Neural Networks with a Principal Component Step
rfe
Backwards Feature Selection
bagFDA
Bagged FDA
sbfControl
Control Object for Selection By Filtering (SBF)
postResample
Calculates performance across resamples
segmentationData
Cell Body Segmentation
confusionMatrix
Create a confusion matrix
lift
Lift Plot
summary.bagEarth
Summarize a bagged earth or FDA fit
varImp
Calculation of variable importance for regression and classification models
predict.knn3
Predictions from k-Nearest Neighbors
caret-internal
Internal Functions
panel.needle
Needle Plot Lattice Panel
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
knn3
k-Nearest Neighbour Classification
print.train
Print Method for the train Class
plot.rfe
Plot RFE Performance Profiles
train_model_list
A List of Available Models in train
prcomp.resamples
Principal Components Analysis of Resampling Results
plot.varImp.train
Plotting variable importance measures
print.confusionMatrix
Print method for confusionMatrix
pottery
Pottery from Pre-Classical Sites in Italy
predictors
List predictors used in the model
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
sbf
Selection By Filtering (SBF)
caretSBF
Selection By Filtering (SBF) Helper Functions
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
cox2
COX-2 Activity Data
as.table.confusionMatrix
Save Confusion Table Results
oneSE
Selecting tuning Parameters
tecator
Fat, Water and Protein Content of Meat Samples
spatialSign
Compute the multivariate spatial sign
downSample
Down- and Up-Sampling Imbalanced Data
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
maxDissim
Maximum Dissimilarity Sampling
update.train
Update or Re-fit a Model
sensitivity
Calculate sensitivity, specificity and predictive values
BoxCoxTrans.default
Box-Cox and Exponential Transformations
train
Fit Predictive Models over Different Tuning Parameters