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caret (version 6.0-57)
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
163,965
Version
6.0-57
License
GPL (>= 2)
Issues
186
Pull Requests
25
Stars
1,650
Forks
628
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
October 11th, 2015
Functions in caret (6.0-57)
Search all functions
bagFDA
Bagged FDA
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
caret-internal
Internal Functions
cars
Kelly Blue Book resale data for 2005 model year GM cars
diff.resamples
Inferential Assessments About Model Performance
dhfr
Dihydrofolate Reductase Inhibitors Data
filterVarImp
Calculation of filter-based variable importance
predict.train
Extract predictions and class probabilities from train objects
twoClassSim
Simulation Functions
calibration
Probability Calibration Plot
gafs_initial
Ancillary genetic algorithm functions
modelLookup
Tools for Models Available in
train
dummyVars
Create A Full Set of Dummy Variables
print.train
Print Method for the train Class
update.train
Update or Re-fit a Model
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
getSamplingInfo
Get sampling info from a train model
knnreg
k-Nearest Neighbour Regression
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
BloodBrain
Blood Brain Barrier Data
bag.default
A General Framework For Bagging
BoxCoxTrans.default
Box-Cox and Exponential Transformations
print.confusionMatrix
Print method for confusionMatrix
createDataPartition
Data Splitting functions
resampleSummary
Summary of resampled performance estimates
trainControl
Control parameters for train
rfeControl
Controlling the Feature Selection Algorithms
as.table.confusionMatrix
Save Confusion Table Results
preProcess
Pre-Processing of Predictors
prcomp.resamples
Principal Components Analysis of Resampling Results
avNNet.default
Neural Networks Using Model Averaging
panel.needle
Needle Plot Lattice Panel
lift
Lift Plot
icr.formula
Independent Component Regression
predictors
List predictors used in the model
var_seq
Sequences of Variables for Tuning
confusionMatrix.train
Estimate a Resampled Confusion Matrix
classDist
Compute and predict the distances to class centroids
caretFuncs
Backwards Feature Selection Helper Functions
index2vec
Convert indicies to a binary vector
plotClassProbs
Plot Predicted Probabilities in Classification Models
sbfControl
Control Object for Selection By Filtering (SBF)
pottery
Pottery from Pre-Classical Sites in Italy
cox2
COX-2 Activity Data
predict.knn3
Predictions from k-Nearest Neighbors
resamples
Collation and Visualization of Resampling Results
dotPlot
Create a dotplot of variable importance values
safs.default
Simulated annealing feature selection
sbf
Selection By Filtering (SBF)
plot.rfe
Plot RFE Performance Profiles
predict.gafs
Predict new samples
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
nearZeroVar
Identification of near zero variance predictors
tecator
Fat, Water and Protein Content of Meat Samples
summary.bagEarth
Summarize a bagged earth or FDA fit
nullModel
Fit a simple, non-informative model
histogram.train
Lattice functions for plotting resampling results
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
knn3
k-Nearest Neighbour Classification
varImp.gafs
Variable importances for GAs and SAs
postResample
Calculates performance across resamples
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
rfe
Backwards Feature Selection
plot.varImp.train
Plotting variable importance measures
maxDissim
Maximum Dissimilarity Sampling
oneSE
Selecting tuning Parameters
varImp
Calculation of variable importance for regression and classification models
confusionMatrix
Create a confusion matrix
findCorrelation
Determine highly correlated variables
oil
Fatty acid composition of commercial oils
GermanCredit
German Credit Data
caretSBF
Selection By Filtering (SBF) Helper Functions
segmentationData
Cell Body Segmentation
format.bagEarth
Format 'bagEarth' objects
predict.bagEarth
Predicted values based on bagged Earth and FDA models
train_model_list
A List of Available Models in train
sensitivity
Calculate sensitivity, specificity and predictive values
spatialSign
Compute the multivariate spatial sign
findLinearCombos
Determine linear combinations in a matrix
plot.gafs
Plot Method for the gafs and safs Classes
bagEarth
Bagged Earth
gafs.default
Genetic algorithm feature selection
safs_initial
Ancillary simulated annealing functions
update.safs
Update or Re-fit a SA or GA Model
pcaNNet.default
Neural Networks with a Principal Component Step
panel.lift2
Lattice Panel Functions for Lift Plots
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
plot.train
Plot Method for the train Class
resampleHist
Plot the resampling distribution of the model statistics
safsControl
Control parameters for GA and SA feature selection
downSample
Down- and Up-Sampling Imbalanced Data
train
Fit Predictive Models over Different Tuning Parameters