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caret (version 6.0-35)
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
6.0-35
License
GPL-2
Maintainer
Max Kuhn
Last Published
August 24th, 2014
Functions in caret (6.0-35)
Search all functions
bag.default
A General Framework For Bagging
confusionMatrix
Create a confusion matrix
predict.knn3
Predictions from k-Nearest Neighbors
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
as.table.confusionMatrix
Save Confusion Table Results
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
BoxCoxTrans.default
Box-Cox and Exponential Transformations
nearZeroVar
Identification of near zero variance predictors
plot.rfe
Plot RFE Performance Profiles
plotClassProbs
Plot Predicted Probabilities in Classification Models
segmentationData
Cell Body Segmentation
spatialSign
Compute the multivariate spatial sign
resampleSummary
Summary of resampled performance estimates
predictors
List predictors used in the model
pottery
Pottery from Pre-Classical Sites in Italy
avNNet.default
Neural Networks Using Model Averaging
nullModel
Fit a simple, non-informative model
filterVarImp
Calculation of filter-based variable importance
caretFuncs
Backwards Feature Selection Helper Functions
confusionMatrix.train
Estimate a Resampled Confusion Matrix
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
twoClassSim
Simulation Functions
dotPlot
Create a dotplot of variable importance values
downSample
Down- and Up-Sampling Imbalanced Data
print.train
Print Method for the train Class
BloodBrain
Blood Brain Barrier Data
format.bagEarth
Format 'bagEarth' objects
resampleHist
Plot the resampling distribution of the model statistics
oneSE
Selecting tuning Parameters
plot.train
Plot Method for the train Class
histogram.train
Lattice functions for plotting resampling results
classDist
Compute and predict the distances to class centroids
sbf
Selection By Filtering (SBF)
GermanCredit
German Credit Data
predict.train
Extract predictions and class probabilities from train objects
pcaNNet.default
Neural Networks with a Principal Component Step
postResample
Calculates performance across resamples
panel.needle
Needle Plot Lattice Panel
modelLookup
Tools for Models Available in
train
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
predict.bagEarth
Predicted values based on bagged Earth and FDA models
cox2
COX-2 Activity Data
icr.formula
Independent Component Regression
rfe
Backwards Feature Selection
dhfr
Dihydrofolate Reductase Inhibitors Data
findLinearCombos
Determine linear combinations in a matrix
knn3
k-Nearest Neighbour Classification
calibration
Probability Calibration Plot
maxDissim
Maximum Dissimilarity Sampling
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
print.confusionMatrix
Print method for confusionMatrix
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
rfeControl
Controlling the Feature Selection Algorithms
preProcess
Pre-Processing of Predictors
train_model_list
A List of Available Models in train
diff.resamples
Inferential Assessments About Model Performance
findCorrelation
Determine highly correlated variables
lift
Lift Plot
resamples
Collation and Visualization of Resampling Results
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
prcomp.resamples
Principal Components Analysis of Resampling Results
tecator
Fat, Water and Protein Content of Meat Samples
varImp
Calculation of variable importance for regression and classification models
summary.bagEarth
Summarize a bagged earth or FDA fit
caretSBF
Selection By Filtering (SBF) Helper Functions
panel.lift2
Lattice Panel Functions for Lift Plots
plot.varImp.train
Plotting variable importance measures
cars
Kelly Blue Book resale data for 2005 model year GM cars
bagEarth
Bagged Earth
oil
Fatty acid composition of commercial oils
sbfControl
Control Object for Selection By Filtering (SBF)
trainControl
Control parameters for train
update.train
Update or Re-fit a Model
bagFDA
Bagged FDA
sensitivity
Calculate sensitivity, specificity and predictive values
knnreg
k-Nearest Neighbour Regression
dummyVars
Create A Full Set of Dummy Variables
caret-internal
Internal Functions
createDataPartition
Data Splitting functions
train
Fit Predictive Models over Different Tuning Parameters