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caret (version 6.0-91)
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
6.0-91
License
GPL (>= 2)
Issues
178
Pull Requests
7
Stars
1,614
Forks
632
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
March 11th, 2022
Functions in caret (6.0-91)
Search all functions
BloodBrain
Blood Brain Barrier Data
calibration
Probability Calibration Plot
avNNet
Neural Networks Using Model Averaging
bagFDA
Bagged FDA
Sacramento
Sacramento CA Home Prices
GermanCredit
German Credit Data
as.matrix.confusionMatrix
Confusion matrix as a table
bag
A General Framework For Bagging
BoxCoxTrans
Box-Cox and Exponential Transformations
bagEarth
Bagged Earth
classDist
Compute and predict the distances to class centroids
densityplot.rfe
Lattice functions for plotting resampling results of recursive feature selection
createDataPartition
Data Splitting functions
caret-internal
Internal Functions
pickSizeBest
Backwards Feature Selection Helper Functions
cox2
COX-2 Activity Data
confusionMatrix.train
Estimate a Resampled Confusion Matrix
downSample
Down- and Up-Sampling Imbalanced Data
dummyVars
Create A Full Set of Dummy Variables
dotPlot
Create a dotplot of variable importance values
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
confusionMatrix
Create a confusion matrix
findLinearCombos
Determine linear combinations in a matrix
findCorrelation
Determine highly correlated variables
gafs_initial
Ancillary genetic algorithm functions
format.bagEarth
Format 'bagEarth' objects
getSamplingInfo
Get sampling info from a train model
cars
Kelly Blue Book resale data for 2005 model year GM cars
filterVarImp
Calculation of filter-based variable importance
gafs.default
Genetic algorithm feature selection
index2vec
Convert indicies to a binary vector
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
caretSBF
Selection By Filtering (SBF) Helper Functions
knnreg
k-Nearest Neighbour Regression
dhfr
Dihydrofolate Reductase Inhibitors Data
diff.resamples
Inferential Assessments About Model Performance
knn3
k-Nearest Neighbour Classification
icr.formula
Independent Component Regression
histogram.train
Lattice functions for plotting resampling results
learning_curve_dat
Create Data to Plot a Learning Curve
oneSE
Selecting tuning Parameters
nullModel
Fit a simple, non-informative model
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
lift
Lift Plot
oil
Fatty acid composition of commercial oils
panel.lift2
Lattice Panel Functions for Lift Plots
modelLookup
Tools for Models Available in
train
nearZeroVar
Identification of near zero variance predictors
train_model_list
A List of Available Models in train
maxDissim
Maximum Dissimilarity Sampling
ggplot.rfe
Plot RFE Performance Profiles
plot.gafs
Plot Method for the gafs and safs Classes
plotClassProbs
Plot Predicted Probabilities in Classification Models
predict.bagEarth
Predicted values based on bagged Earth and FDA models
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
print.confusionMatrix
Print method for confusionMatrix
plot.varImp.train
Plotting variable importance measures
defaultSummary
Calculates performance across resamples
predictors
List predictors used in the model
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
ggplot.train
Plot Method for the train Class
preProcess
Pre-Processing of Predictors
pcaNNet
Neural Networks with a Principal Component Step
panel.needle
Needle Plot Lattice Panel
predict.gafs
Predict new samples
pottery
Pottery from Pre-Classical Sites in Italy
resampleSummary
Summary of resampled performance estimates
resampleHist
Plot the resampling distribution of the model statistics
safs_initial
Ancillary simulated annealing functions
predict.knn3
Predictions from k-Nearest Neighbors
gafsControl
Control parameters for GA and SA feature selection
rfe
Backwards Feature Selection
resamples
Collation and Visualization of Resampling Results
prcomp.resamples
Principal Components Analysis of Resampling Results
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
recall
Calculate recall, precision and F values
extractPrediction
Extract predictions and class probabilities from train objects
print.train
Print Method for the train Class
rfeControl
Controlling the Feature Selection Algorithms
thresholder
Generate Data to Choose a Probability Threshold
summary.bagEarth
Summarize a bagged earth or FDA fit
tecator
Fat, Water and Protein Content of Meat Samples
train
Fit Predictive Models over Different Tuning Parameters
sbf
Selection By Filtering (SBF)
safs
Simulated annealing feature selection
sbfControl
Control Object for Selection By Filtering (SBF)
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
segmentationData
Cell Body Segmentation
var_seq
Sequences of Variables for Tuning
scat
Morphometric Data on Scat
update.safs
Update or Re-fit a SA or GA Model
update.train
Update or Re-fit a Model
varImp.gafs
Variable importances for GAs and SAs
spatialSign
Compute the multivariate spatial sign
varImp
Calculation of variable importance for regression and classification models
negPredValue
Calculate sensitivity, specificity and predictive values
trainControl
Control parameters for train
SLC14_1
Simulation Functions