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caret (version 6.0-52)
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
6.0-52
License
GPL (>= 2)
Issues
186
Pull Requests
25
Stars
1,650
Forks
628
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
July 17th, 2015
Functions in caret (6.0-52)
Search all functions
BoxCoxTrans.default
Box-Cox and Exponential Transformations
dummyVars
Create A Full Set of Dummy Variables
filterVarImp
Calculation of filter-based variable importance
maxDissim
Maximum Dissimilarity Sampling
pcaNNet.default
Neural Networks with a Principal Component Step
plot.train
Plot Method for the train Class
predict.bagEarth
Predicted values based on bagged Earth and FDA models
findLinearCombos
Determine linear combinations in a matrix
histogram.train
Lattice functions for plotting resampling results
lift
Lift Plot
confusionMatrix
Create a confusion matrix
nearZeroVar
Identification of near zero variance predictors
dotPlot
Create a dotplot of variable importance values
BloodBrain
Blood Brain Barrier Data
cars
Kelly Blue Book resale data for 2005 model year GM cars
gafs.default
Genetic algorithm feature selection
caret-internal
Internal Functions
GermanCredit
German Credit Data
cox2
COX-2 Activity Data
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
print.confusionMatrix
Print method for confusionMatrix
bag.default
A General Framework For Bagging
dhfr
Dihydrofolate Reductase Inhibitors Data
predictors
List predictors used in the model
format.bagEarth
Format 'bagEarth' objects
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
nullModel
Fit a simple, non-informative model
prcomp.resamples
Principal Components Analysis of Resampling Results
predict.train
Extract predictions and class probabilities from train objects
diff.resamples
Inferential Assessments About Model Performance
confusionMatrix.train
Estimate a Resampled Confusion Matrix
oil
Fatty acid composition of commercial oils
getSamplingInfo
Get sampling info from a train model
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
knn3
k-Nearest Neighbour Classification
knnreg
k-Nearest Neighbour Regression
gafs_initial
Ancillary genetic algorithm functions
downSample
Down- and Up-Sampling Imbalanced Data
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
plot.gafs
Plot Method for the gafs and safs Classes
index2vec
Convert indicies to a binary vector
bagFDA
Bagged FDA
train_model_list
A List of Available Models in train
panel.needle
Needle Plot Lattice Panel
panel.lift2
Lattice Panel Functions for Lift Plots
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
summary.bagEarth
Summarize a bagged earth or FDA fit
plot.rfe
Plot RFE Performance Profiles
modelLookup
Tools for Models Available in
train
predict.knn3
Predictions from k-Nearest Neighbors
resamples
Collation and Visualization of Resampling Results
resampleHist
Plot the resampling distribution of the model statistics
caretFuncs
Backwards Feature Selection Helper Functions
pottery
Pottery from Pre-Classical Sites in Italy
findCorrelation
Determine highly correlated variables
calibration
Probability Calibration Plot
icr.formula
Independent Component Regression
postResample
Calculates performance across resamples
plotClassProbs
Plot Predicted Probabilities in Classification Models
tecator
Fat, Water and Protein Content of Meat Samples
rfe
Backwards Feature Selection
var_seq
Sequences of Variables for Tuning
safs.default
Simulated annealing feature selection
safs_initial
Ancillary simulated annealing functions
resampleSummary
Summary of resampled performance estimates
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
predict.gafs
Predict new samples
print.train
Print Method for the train Class
spatialSign
Compute the multivariate spatial sign
sensitivity
Calculate sensitivity, specificity and predictive values
sbfControl
Control Object for Selection By Filtering (SBF)
update.safs
Update or Re-fit a SA or GA Model
caretSBF
Selection By Filtering (SBF) Helper Functions
plot.varImp.train
Plotting variable importance measures
oneSE
Selecting tuning Parameters
rfeControl
Controlling the Feature Selection Algorithms
varImp.gafs
Variable importances for GAs and SAs
safsControl
Control parameters for GA and SA feature selection
segmentationData
Cell Body Segmentation
sbf
Selection By Filtering (SBF)
trainControl
Control parameters for train
varImp
Calculation of variable importance for regression and classification models
preProcess
Pre-Processing of Predictors
createDataPartition
Data Splitting functions
update.train
Update or Re-fit a Model
twoClassSim
Simulation Functions
bagEarth
Bagged Earth
classDist
Compute and predict the distances to class centroids
as.table.confusionMatrix
Save Confusion Table Results
avNNet.default
Neural Networks Using Model Averaging
train
Fit Predictive Models over Different Tuning Parameters