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caret (version 6.0-68)
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
138,220
Version
6.0-68
License
GPL (>= 2)
Issues
193
Pull Requests
26
Stars
1,671
Forks
630
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
April 11th, 2016
Functions in caret (6.0-68)
Search all functions
BloodBrain
Blood Brain Barrier Data
avNNet.default
Neural Networks Using Model Averaging
nullModel
Fit a simple, non-informative model
BoxCoxTrans.default
Box-Cox and Exponential Transformations
maxDissim
Maximum Dissimilarity Sampling
bag.default
A General Framework For Bagging
GermanCredit
German Credit Data
dotPlot
Create a dotplot of variable importance values
confusionMatrix.train
Estimate a Resampled Confusion Matrix
downSample
Down- and Up-Sampling Imbalanced Data
plot.gafs
Plot Method for the gafs and safs Classes
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
gafs.default
Genetic algorithm feature selection
cox2
COX-2 Activity Data
plotClassProbs
Plot Predicted Probabilities in Classification Models
index2vec
Convert indicies to a binary vector
classDist
Compute and predict the distances to class centroids
Sacramento
Sacramento CA Home Prices
findCorrelation
Determine highly correlated variables
pcaNNet.default
Neural Networks with a Principal Component Step
predict.train
Extract predictions and class probabilities from train objects
caret-internal
Internal Functions
createDataPartition
Data Splitting functions
confusionMatrix
Create a confusion matrix
calibration
Probability Calibration Plot
lift
Lift Plot
plot.rfe
Plot RFE Performance Profiles
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
safs_initial
Ancillary simulated annealing functions
gafs_initial
Ancillary genetic algorithm functions
update.safs
Update or Re-fit a SA or GA Model
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
filterVarImp
Calculation of filter-based variable importance
panel.needle
Needle Plot Lattice Panel
spatialSign
Compute the multivariate spatial sign
dummyVars
Create A Full Set of Dummy Variables
predict.gafs
Predict new samples
knnreg
k-Nearest Neighbour Regression
postResample
Calculates performance across resamples
dhfr
Dihydrofolate Reductase Inhibitors Data
varImp
Calculation of variable importance for regression and classification models
findLinearCombos
Determine linear combinations in a matrix
resamples
Collation and Visualization of Resampling Results
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
safsControl
Control parameters for GA and SA feature selection
rfe
Backwards Feature Selection
resampleHist
Plot the resampling distribution of the model statistics
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
plot.train
Plot Method for the train Class
trainControl
Control parameters for train
sbfControl
Control Object for Selection By Filtering (SBF)
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
cars
Kelly Blue Book resale data for 2005 model year GM cars
oil
Fatty acid composition of commercial oils
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
segmentationData
Cell Body Segmentation
pottery
Pottery from Pre-Classical Sites in Italy
panel.lift2
Lattice Panel Functions for Lift Plots
train_model_list
A List of Available Models in train
print.confusionMatrix
Print method for confusionMatrix
histogram.train
Lattice functions for plotting resampling results
getSamplingInfo
Get sampling info from a train model
knn3
k-Nearest Neighbour Classification
learing_curve_dat
Create Data to Plot a Learning Curve
predictors
List predictors used in the model
var_seq
Sequences of Variables for Tuning
rfeControl
Controlling the Feature Selection Algorithms
sbf
Selection By Filtering (SBF)
predict.knn3
Predictions from k-Nearest Neighbors
predict.bagEarth
Predicted values based on bagged Earth and FDA models
twoClassSim
Simulation Functions
bagFDA
Bagged FDA
caretSBF
Selection By Filtering (SBF) Helper Functions
format.bagEarth
Format 'bagEarth' objects
preProcess
Pre-Processing of Predictors
summary.bagEarth
Summarize a bagged earth or FDA fit
oneSE
Selecting tuning Parameters
safs.default
Simulated annealing feature selection
tecator
Fat, Water and Protein Content of Meat Samples
print.train
Print Method for the train Class
icr.formula
Independent Component Regression
prcomp.resamples
Principal Components Analysis of Resampling Results
caretFuncs
Backwards Feature Selection Helper Functions
update.train
Update or Re-fit a Model
as.table.confusionMatrix
Save Confusion Table Results
bagEarth
Bagged Earth
modelLookup
Tools for Models Available in
train
nearZeroVar
Identification of near zero variance predictors
diff.resamples
Inferential Assessments About Model Performance
resampleSummary
Summary of resampled performance estimates
varImp.gafs
Variable importances for GAs and SAs
sensitivity
Calculate sensitivity, specificity and predictive values
plot.varImp.train
Plotting variable importance measures
train
Fit Predictive Models over Different Tuning Parameters