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caret (version 6.0-71)
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
208,749
Version
6.0-71
License
GPL (>= 2)
Issues
179
Pull Requests
7
Stars
1,617
Forks
633
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
August 5th, 2016
Functions in caret (6.0-71)
Search all functions
calibration
Probability Calibration Plot
BoxCoxTrans.default
Box-Cox and Exponential Transformations
bag.default
A General Framework For Bagging
bagEarth
Bagged Earth
cars
Kelly Blue Book resale data for 2005 model year GM cars
BloodBrain
Blood Brain Barrier Data
bagFDA
Bagged FDA
caret-internal
Internal Functions
as.table.confusionMatrix
Save Confusion Table Results
avNNet.default
Neural Networks Using Model Averaging
dhfr
Dihydrofolate Reductase Inhibitors Data
dotPlot
Create a dotplot of variable importance values
diff.resamples
Inferential Assessments About Model Performance
createDataPartition
Data Splitting functions
classDist
Compute and predict the distances to class centroids
dummyVars
Create A Full Set of Dummy Variables
downSample
Down- and Up-Sampling Imbalanced Data
confusionMatrix.train
Estimate a Resampled Confusion Matrix
cox2
COX-2 Activity Data
predict.train
Extract predictions and class probabilities from train objects
getSamplingInfo
Get sampling info from a train model
findLinearCombos
Determine linear combinations in a matrix
findCorrelation
Determine highly correlated variables
gafs_initial
Ancillary genetic algorithm functions
filterVarImp
Calculation of filter-based variable importance
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
gafs.default
Genetic algorithm feature selection
GermanCredit
German Credit Data
icr.formula
Independent Component Regression
format.bagEarth
Format 'bagEarth' objects
histogram.train
Lattice functions for plotting resampling results
index2vec
Convert indicies to a binary vector
learing_curve_dat
Create Data to Plot a Learning Curve
maxDissim
Maximum Dissimilarity Sampling
knnreg
k-Nearest Neighbour Regression
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
knn3
k-Nearest Neighbour Classification
lift
Lift Plot
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
nullModel
Fit a simple, non-informative model
panel.needle
Needle Plot Lattice Panel
pcaNNet.default
Neural Networks with a Principal Component Step
modelLookup
Tools for Models Available in
train
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
nearZeroVar
Identification of near zero variance predictors
oil
Fatty acid composition of commercial oils
panel.lift2
Lattice Panel Functions for Lift Plots
plot.gafs
Plot Method for the gafs and safs Classes
train_model_list
A List of Available Models in train
plot.rfe
Plot RFE Performance Profiles
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
predict.gafs
Predict new samples
plot.train
Plot Method for the train Class
plot.varImp.train
Plotting variable importance measures
prcomp.resamples
Principal Components Analysis of Resampling Results
pottery
Pottery from Pre-Classical Sites in Italy
plotClassProbs
Plot Predicted Probabilities in Classification Models
predict.bagEarth
Predicted values based on bagged Earth and FDA models
predict.knn3
Predictions from k-Nearest Neighbors
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
safsControl
Control parameters for GA and SA feature selection
sbf
Selection By Filtering (SBF)
spatialSign
Compute the multivariate spatial sign
oneSE
Selecting tuning Parameters
print.train
Print Method for the train Class
print.confusionMatrix
Print method for confusionMatrix
sbfControl
Control Object for Selection By Filtering (SBF)
caretSBF
Selection By Filtering (SBF) Helper Functions
trainControl
Control parameters for train
twoClassSim
Simulation Functions
varImp.gafs
Variable importances for GAs and SAs
varImp
Calculation of variable importance for regression and classification models
resamples
Collation and Visualization of Resampling Results
resampleHist
Plot the resampling distribution of the model statistics
resampleSummary
Summary of resampled performance estimates
var_seq
Sequences of Variables for Tuning
rfe
Backwards Feature Selection
rfeControl
Controlling the Feature Selection Algorithms
summary.bagEarth
Summarize a bagged earth or FDA fit
tecator
Fat, Water and Protein Content of Meat Samples
Sacramento
Sacramento CA Home Prices
scat
Morphometric Data on Scat
safs.default
Simulated annealing feature selection
segmentationData
Cell Body Segmentation
predictors
List predictors used in the model
preProcess
Pre-Processing of Predictors
safs_initial
Ancillary simulated annealing functions
caretFuncs
Backwards Feature Selection Helper Functions
update.safs
Update or Re-fit a SA or GA Model
update.train
Update or Re-fit a Model