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caret (version 6.0-70)
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
6.0-70
License
GPL (>= 2)
Issues
186
Pull Requests
25
Stars
1,650
Forks
628
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
June 13th, 2016
Functions in caret (6.0-70)
Search all functions
bag.default
A General Framework For Bagging
as.table.confusionMatrix
Save Confusion Table Results
cars
Kelly Blue Book resale data for 2005 model year GM cars
calibration
Probability Calibration Plot
BoxCoxTrans.default
Box-Cox and Exponential Transformations
caret-internal
Internal Functions
BloodBrain
Blood Brain Barrier Data
avNNet.default
Neural Networks Using Model Averaging
bagEarth
Bagged Earth
bagFDA
Bagged FDA
dummyVars
Create A Full Set of Dummy Variables
cox2
COX-2 Activity Data
classDist
Compute and predict the distances to class centroids
createDataPartition
Data Splitting functions
predict.train
Extract predictions and class probabilities from train objects
dotPlot
Create a dotplot of variable importance values
confusionMatrix.train
Estimate a Resampled Confusion Matrix
dhfr
Dihydrofolate Reductase Inhibitors Data
downSample
Down- and Up-Sampling Imbalanced Data
diff.resamples
Inferential Assessments About Model Performance
format.bagEarth
Format 'bagEarth' objects
GermanCredit
German Credit Data
getSamplingInfo
Get sampling info from a train model
findLinearCombos
Determine linear combinations in a matrix
gafs_initial
Ancillary genetic algorithm functions
icr.formula
Independent Component Regression
gafs.default
Genetic algorithm feature selection
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
findCorrelation
Determine highly correlated variables
filterVarImp
Calculation of filter-based variable importance
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
maxDissim
Maximum Dissimilarity Sampling
index2vec
Convert indicies to a binary vector
knnreg
k-Nearest Neighbour Regression
histogram.train
Lattice functions for plotting resampling results
learing_curve_dat
Create Data to Plot a Learning Curve
knn3
k-Nearest Neighbour Classification
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
lift
Lift Plot
nullModel
Fit a simple, non-informative model
panel.needle
Needle Plot Lattice Panel
modelLookup
Tools for Models Available in
train
oil
Fatty acid composition of commercial oils
plot.gafs
Plot Method for the gafs and safs Classes
nearZeroVar
Identification of near zero variance predictors
pcaNNet.default
Neural Networks with a Principal Component Step
panel.lift2
Lattice Panel Functions for Lift Plots
train_model_list
A List of Available Models in train
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
plot.rfe
Plot RFE Performance Profiles
plot.varImp.train
Plotting variable importance measures
prcomp.resamples
Principal Components Analysis of Resampling Results
plot.train
Plot Method for the train Class
pottery
Pottery from Pre-Classical Sites in Italy
plotClassProbs
Plot Predicted Probabilities in Classification Models
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
predict.bagEarth
Predicted values based on bagged Earth and FDA models
predict.gafs
Predict new samples
predict.knn3
Predictions from k-Nearest Neighbors
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
resampleSummary
Summary of resampled performance estimates
var_seq
Sequences of Variables for Tuning
print.train
Print Method for the train Class
print.confusionMatrix
Print method for confusionMatrix
resampleHist
Plot the resampling distribution of the model statistics
resamples
Collation and Visualization of Resampling Results
predictors
List predictors used in the model
preProcess
Pre-Processing of Predictors
sbf
Selection By Filtering (SBF)
safsControl
Control parameters for GA and SA feature selection
sbfControl
Control Object for Selection By Filtering (SBF)
safs.default
Simulated annealing feature selection
Sacramento
Sacramento CA Home Prices
caretSBF
Selection By Filtering (SBF) Helper Functions
rfe
Backwards Feature Selection
rfeControl
Controlling the Feature Selection Algorithms
caretFuncs
Backwards Feature Selection Helper Functions
safs_initial
Ancillary simulated annealing functions
oneSE
Selecting tuning Parameters
segmentationData
Cell Body Segmentation
update.safs
Update or Re-fit a SA or GA Model
twoClassSim
Simulation Functions
tecator
Fat, Water and Protein Content of Meat Samples
trainControl
Control parameters for train
update.train
Update or Re-fit a Model
varImp.gafs
Variable importances for GAs and SAs
varImp
Calculation of variable importance for regression and classification models
spatialSign
Compute the multivariate spatial sign
summary.bagEarth
Summarize a bagged earth or FDA fit