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caret (version 6.0-37)
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
158,845
Version
6.0-37
License
GPL-2
Maintainer
Max Kuhn
Last Published
November 10th, 2014
Functions in caret (6.0-37)
Search all functions
cox2
COX-2 Activity Data
downSample
Down- and Up-Sampling Imbalanced Data
findCorrelation
Determine highly correlated variables
BloodBrain
Blood Brain Barrier Data
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
avNNet.default
Neural Networks Using Model Averaging
dummyVars
Create A Full Set of Dummy Variables
filterVarImp
Calculation of filter-based variable importance
plot.rfe
Plot RFE Performance Profiles
GermanCredit
German Credit Data
rfeControl
Controlling the Feature Selection Algorithms
createDataPartition
Data Splitting functions
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
bag.default
A General Framework For Bagging
postResample
Calculates performance across resamples
nullModel
Fit a simple, non-informative model
cars
Kelly Blue Book resale data for 2005 model year GM cars
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
rfe
Backwards Feature Selection
plot.varImp.train
Plotting variable importance measures
predict.bagEarth
Predicted values based on bagged Earth and FDA models
predict.knn3
Predictions from k-Nearest Neighbors
maxDissim
Maximum Dissimilarity Sampling
icr.formula
Independent Component Regression
BoxCoxTrans.default
Box-Cox and Exponential Transformations
oil
Fatty acid composition of commercial oils
knnreg
k-Nearest Neighbour Regression
preProcess
Pre-Processing of Predictors
predict.train
Extract predictions and class probabilities from train objects
plot.train
Plot Method for the train Class
as.table.confusionMatrix
Save Confusion Table Results
format.bagEarth
Format 'bagEarth' objects
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
classDist
Compute and predict the distances to class centroids
prcomp.resamples
Principal Components Analysis of Resampling Results
predictors
List predictors used in the model
resamples
Collation and Visualization of Resampling Results
confusionMatrix.train
Estimate a Resampled Confusion Matrix
knn3
k-Nearest Neighbour Classification
spatialSign
Compute the multivariate spatial sign
panel.needle
Needle Plot Lattice Panel
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
lift
Lift Plot
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
dotPlot
Create a dotplot of variable importance values
dhfr
Dihydrofolate Reductase Inhibitors Data
caret-internal
Internal Functions
sbf
Selection By Filtering (SBF)
resampleSummary
Summary of resampled performance estimates
caretFuncs
Backwards Feature Selection Helper Functions
print.confusionMatrix
Print method for confusionMatrix
plotClassProbs
Plot Predicted Probabilities in Classification Models
confusionMatrix
Create a confusion matrix
train_model_list
A List of Available Models in train
histogram.train
Lattice functions for plotting resampling results
bagFDA
Bagged FDA
modelLookup
Tools for Models Available in
train
varImp
Calculation of variable importance for regression and classification models
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
twoClassSim
Simulation Functions
panel.lift2
Lattice Panel Functions for Lift Plots
pcaNNet.default
Neural Networks with a Principal Component Step
sbfControl
Control Object for Selection By Filtering (SBF)
tecator
Fat, Water and Protein Content of Meat Samples
pottery
Pottery from Pre-Classical Sites in Italy
resampleHist
Plot the resampling distribution of the model statistics
caretSBF
Selection By Filtering (SBF) Helper Functions
print.train
Print Method for the train Class
findLinearCombos
Determine linear combinations in a matrix
update.train
Update or Re-fit a Model
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
trainControl
Control parameters for train
summary.bagEarth
Summarize a bagged earth or FDA fit
nearZeroVar
Identification of near zero variance predictors
sensitivity
Calculate sensitivity, specificity and predictive values
oneSE
Selecting tuning Parameters
diff.resamples
Inferential Assessments About Model Performance
calibration
Probability Calibration Plot
segmentationData
Cell Body Segmentation
bagEarth
Bagged Earth
train
Fit Predictive Models over Different Tuning Parameters