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caret (version 6.0-90)
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
160,352
Version
6.0-90
License
GPL (>= 2)
Issues
172
Pull Requests
5
Stars
1,585
Forks
630
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
October 9th, 2021
Functions in caret (6.0-90)
Search functions
as.matrix.confusionMatrix
Confusion matrix as a table
BoxCoxTrans
Box-Cox and Exponential Transformations
bagEarth
Bagged Earth
calibration
Probability Calibration Plot
bagFDA
Bagged FDA
bag
A General Framework For Bagging
Sacramento
Sacramento CA Home Prices
avNNet
Neural Networks Using Model Averaging
BloodBrain
Blood Brain Barrier Data
GermanCredit
German Credit Data
confusionMatrix
Create a confusion matrix
confusionMatrix.train
Estimate a Resampled Confusion Matrix
classDist
Compute and predict the distances to class centroids
caretSBF
Selection By Filtering (SBF) Helper Functions
cox2
COX-2 Activity Data
caret-internal
Internal Functions
downSample
Down- and Up-Sampling Imbalanced Data
cars
Kelly Blue Book resale data for 2005 model year GM cars
dummyVars
Create A Full Set of Dummy Variables
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
format.bagEarth
Format 'bagEarth' objects
gafs.default
Genetic algorithm feature selection
filterVarImp
Calculation of filter-based variable importance
index2vec
Convert indicies to a binary vector
dotPlot
Create a dotplot of variable importance values
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
icr.formula
Independent Component Regression
histogram.train
Lattice functions for plotting resampling results
createDataPartition
Data Splitting functions
dhfr
Dihydrofolate Reductase Inhibitors Data
densityplot.rfe
Lattice functions for plotting resampling results of recursive feature selection
knnreg
k-Nearest Neighbour Regression
knn3
k-Nearest Neighbour Classification
learning_curve_dat
Create Data to Plot a Learning Curve
findLinearCombos
Determine linear combinations in a matrix
findCorrelation
Determine highly correlated variables
gafs_initial
Ancillary genetic algorithm functions
getSamplingInfo
Get sampling info from a train model
pickSizeBest
Backwards Feature Selection Helper Functions
diff.resamples
Inferential Assessments About Model Performance
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
oil
Fatty acid composition of commercial oils
nullModel
Fit a simple, non-informative model
train_model_list
A List of Available Models in train
oneSE
Selecting tuning Parameters
nearZeroVar
Identification of near zero variance predictors
panel.lift2
Lattice Panel Functions for Lift Plots
lift
Lift Plot
maxDissim
Maximum Dissimilarity Sampling
ggplot.train
Plot Method for the train Class
plot.varImp.train
Plotting variable importance measures
panel.needle
Needle Plot Lattice Panel
ggplot.rfe
Plot RFE Performance Profiles
plot.gafs
Plot Method for the gafs and safs Classes
pcaNNet
Neural Networks with a Principal Component Step
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
modelLookup
Tools for Models Available in
train
defaultSummary
Calculates performance across resamples
predictors
List predictors used in the model
predict.gafs
Predict new samples
plotClassProbs
Plot Predicted Probabilities in Classification Models
print.confusionMatrix
Print method for confusionMatrix
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
pottery
Pottery from Pre-Classical Sites in Italy
extractPrediction
Extract predictions and class probabilities from train objects
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
predict.knn3
Predictions from k-Nearest Neighbors
gafsControl
Control parameters for GA and SA feature selection
preProcess
Pre-Processing of Predictors
prcomp.resamples
Principal Components Analysis of Resampling Results
resamples
Collation and Visualization of Resampling Results
predict.bagEarth
Predicted values based on bagged Earth and FDA models
safs_initial
Ancillary simulated annealing functions
print.train
Print Method for the train Class
recall
Calculate recall, precision and F values
rfe
Backwards Feature Selection
resampleSummary
Summary of resampled performance estimates
resampleHist
Plot the resampling distribution of the model statistics
thresholder
Generate Data to Choose a Probability Threshold
tecator
Fat, Water and Protein Content of Meat Samples
train
Fit Predictive Models over Different Tuning Parameters
sbf
Selection By Filtering (SBF)
varImp
Calculation of variable importance for regression and classification models
sbfControl
Control Object for Selection By Filtering (SBF)
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
summary.bagEarth
Summarize a bagged earth or FDA fit
var_seq
Sequences of Variables for Tuning
negPredValue
Calculate sensitivity, specificity and predictive values
scat
Morphometric Data on Scat
varImp.gafs
Variable importances for GAs and SAs
rfeControl
Controlling the Feature Selection Algorithms
safs
Simulated annealing feature selection
spatialSign
Compute the multivariate spatial sign
SLC14_1
Simulation Functions
trainControl
Control parameters for train
segmentationData
Cell Body Segmentation
update.safs
Update or Re-fit a SA or GA Model
update.train
Update or Re-fit a Model