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caret (version 6.0-73)
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
221,361
Version
6.0-73
License
GPL (>= 2)
Issues
180
Pull Requests
4
Stars
1,619
Forks
632
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
November 10th, 2016
Functions in caret (6.0-73)
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bag
A General Framework For Bagging
pickSizeBest
Backwards Feature Selection Helper Functions
caretSBF
Selection By Filtering (SBF) Helper Functions
bagFDA
Bagged FDA
BoxCoxTrans
Box-Cox and Exponential Transformations
calibration
Probability Calibration Plot
bagEarth
Bagged Earth
caret-internal
Internal Functions
downSample
Down- and Up-Sampling Imbalanced Data
confusionMatrix.train
Estimate a Resampled Confusion Matrix
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
createDataPartition
Data Splitting functions
dotPlot
Create a dotplot of variable importance values
densityplot.rfe
Lattice functions for plotting resampling results of recursive feature selection
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
dummyVars
Create A Full Set of Dummy Variables
classDist
Compute and predict the distances to class centroids
diff.resamples
Inferential Assessments About Model Performance
GermanCredit
German Credit Data
findLinearCombos
Determine linear combinations in a matrix
gafs_initial
Ancillary genetic algorithm functions
getSamplingInfo
Get sampling info from a train model
gafs.default
Genetic algorithm feature selection
format.bagEarth
Format 'bagEarth' objects
filterVarImp
Calculation of filter-based variable importance
findCorrelation
Determine highly correlated variables
histogram.train
Lattice functions for plotting resampling results
icr.formula
Independent Component Regression
lift
Lift Plot
maxDissim
Maximum Dissimilarity Sampling
learing_curve_dat
Create Data to Plot a Learning Curve
knn3
k-Nearest Neighbour Classification
modelLookup
Tools for Models Available in
train
nullModel
Fit a simple, non-informative model
nearZeroVar
Identification of near zero variance predictors
index2vec
Convert indicies to a binary vector
knnreg
k-Nearest Neighbour Regression
pcaNNet
Neural Networks with a Principal Component Step
train_model_list
A List of Available Models in train
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
panel.lift2
Lattice Panel Functions for Lift Plots
plot.varImp.train
Plotting variable importance measures
panel.needle
Needle Plot Lattice Panel
plotClassProbs
Plot Predicted Probabilities in Classification Models
ggplot.train
Plot Method for the train Class
plot.gafs
Plot Method for the gafs and safs Classes
ggplot.rfe
Plot RFE Performance Profiles
preProcess
Pre-Processing of Predictors
oneSE
Selecting tuning Parameters
print.confusionMatrix
Print method for confusionMatrix
extractPrediction
Extract predictions and class probabilities from train objects
predict.bagEarth
Predicted values based on bagged Earth and FDA models
predictors
List predictors used in the model
predict.knn3
Predictions from k-Nearest Neighbors
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
predict.gafs
Predict new samples
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
resamples
Collation and Visualization of Resampling Results
resampleSummary
Summary of resampled performance estimates
prcomp.resamples
Principal Components Analysis of Resampling Results
as.matrix.confusionMatrix
Confusion matrix as a table
resampleHist
Plot the resampling distribution of the model statistics
avNNet
Neural Networks Using Model Averaging
print.train
Print Method for the train Class
var_seq
Sequences of Variables for Tuning
varImp.gafs
Variable importances for GAs and SAs
gafsControl
Control parameters for GA and SA feature selection
sbf
Selection By Filtering (SBF)
update.safs
Update or Re-fit a SA or GA Model
update.train
Update or Re-fit a Model
trainControl
Control parameters for train
rfeControl
Controlling the Feature Selection Algorithms
SLC14_1
Simulation Functions
sbfControl
Control Object for Selection By Filtering (SBF)
rfe
Backwards Feature Selection
varImp
Calculation of variable importance for regression and classification models
spatialSign
Compute the multivariate spatial sign
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
safs_initial
Ancillary simulated annealing functions
safs
Simulated annealing feature selection
summary.bagEarth
Summarize a bagged earth or FDA fit
train
Fit Predictive Models over Different Tuning Parameters