Rdocumentation
powered by
Learn R Programming
⚠️
There's a newer version (6.0-94) of this package.
Take me there.
caret (version 6.0-88)
Classification and Regression Training
Description
Misc functions for training and plotting classification and regression models.
Copy Link
Link to current version
Version
Version
6.0-94
6.0-93
6.0-92
6.0-91
6.0-90
6.0-89
6.0-88
6.0-86
6.0-85
6.0-84
6.0-83
6.0-82
6.0-81
6.0-80
6.0-79
6.0-78
6.0-77
6.0-76
6.0-73
6.0-72
6.0-71
6.0-70
6.0-68
6.0-64
6.0-62
6.0-58
6.0-57
6.0-52
6.0-47
6.0-41
6.0-37
6.0-35
6.0-34
6.0-30
6.0-29
6.0-24
6.0-22
6.0-21
5.17-7
5.16-24
5.16-04
5.15-61
5.15-052
5.15-048
5.15-045
5.15-044
5.15-023
5.14-023
5.13-037
5.13-20
5.12-04
5.11-06
5.10-13
5.09-012
5.09-006
5.08-011
5.07-024
5.07-005
5.07-001
5.05.004
5.04-007
5.03-003
5.02-011
5.01-001
4.99
4.98
4.92
4.91
4.90
4.89
4.88
4.87
4.85
4.83
4.78
4.77
4.76
4.75
4.73
4.72
4.70
4.69
4.68
4.67
4.65
4.64
4.63
4.62
4.61
4.60
4.59
4.58
4.57
4.54
4.53
4.51
4.49
4.48
4.47
4.45
4.44
4.43
4.42
4.41
4.39
4.37
4.36
4.34
4.33
4.31
4.30
4.27
4.26
4.25
4.24
4.23
4.20
4.19
4.18
4.17
4.16
4.15
4.12
4.11
4.10
4.08
4.06
4.05
3.51
3.45
3.37
3.32
3.25
3.21
3.16
3.13
3.12
3.08
2.29
2.27
Install
install.packages('caret')
Monthly Downloads
148,125
Version
6.0-88
License
GPL (>= 2)
Issues
177
Pull Requests
7
Stars
1,613
Forks
632
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
May 15th, 2021
Functions in caret (6.0-88)
Search all functions
BoxCoxTrans
Box-Cox and Exponential Transformations
Sacramento
Sacramento CA Home Prices
GermanCredit
German Credit Data
BloodBrain
Blood Brain Barrier Data
bagFDA
Bagged FDA
avNNet
Neural Networks Using Model Averaging
as.matrix.confusionMatrix
Confusion matrix as a table
calibration
Probability Calibration Plot
bag
A General Framework For Bagging
createDataPartition
Data Splitting functions
bagEarth
Bagged Earth
dotPlot
Create a dotplot of variable importance values
densityplot.rfe
Lattice functions for plotting resampling results of recursive feature selection
caret-internal
Internal Functions
diff.resamples
Inferential Assessments About Model Performance
dhfr
Dihydrofolate Reductase Inhibitors Data
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
pickSizeBest
Backwards Feature Selection Helper Functions
confusionMatrix.train
Estimate a Resampled Confusion Matrix
cox2
COX-2 Activity Data
cars
Kelly Blue Book resale data for 2005 model year GM cars
caretSBF
Selection By Filtering (SBF) Helper Functions
format.bagEarth
Format 'bagEarth' objects
confusionMatrix
Create a confusion matrix
classDist
Compute and predict the distances to class centroids
gafs.default
Genetic algorithm feature selection
findCorrelation
Determine highly correlated variables
histogram.train
Lattice functions for plotting resampling results
findLinearCombos
Determine linear combinations in a matrix
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
icr.formula
Independent Component Regression
index2vec
Convert indicies to a binary vector
knn3
k-Nearest Neighbour Classification
filterVarImp
Calculation of filter-based variable importance
gafs_initial
Ancillary genetic algorithm functions
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
modelLookup
Tools for Models Available in
train
oneSE
Selecting tuning Parameters
summary.bagEarth
Summarize a bagged earth or FDA fit
downSample
Down- and Up-Sampling Imbalanced Data
panel.needle
Needle Plot Lattice Panel
ggplot.train
Plot Method for the train Class
dummyVars
Create A Full Set of Dummy Variables
pcaNNet
Neural Networks with a Principal Component Step
knnreg
k-Nearest Neighbour Regression
tecator
Fat, Water and Protein Content of Meat Samples
varImp.gafs
Variable importances for GAs and SAs
predictors
List predictors used in the model
oil
Fatty acid composition of commercial oils
rfeControl
Controlling the Feature Selection Algorithms
learning_curve_dat
Create Data to Plot a Learning Curve
varImp
Calculation of variable importance for regression and classification models
getSamplingInfo
Get sampling info from a train model
print.confusionMatrix
Print method for confusionMatrix
nullModel
Fit a simple, non-informative model
predict.bagEarth
Predicted values based on bagged Earth and FDA models
panel.lift2
Lattice Panel Functions for Lift Plots
plot.gafs
Plot Method for the gafs and safs Classes
ggplot.rfe
Plot RFE Performance Profiles
preProcess
Pre-Processing of Predictors
nearZeroVar
Identification of near zero variance predictors
safs
Simulated annealing feature selection
train_model_list
A List of Available Models in train
plotClassProbs
Plot Predicted Probabilities in Classification Models
sbf
Selection By Filtering (SBF)
sbfControl
Control Object for Selection By Filtering (SBF)
gafsControl
Control parameters for GA and SA feature selection
spatialSign
Compute the multivariate spatial sign
negPredValue
Calculate sensitivity, specificity and predictive values
safs_initial
Ancillary simulated annealing functions
extractPrediction
Extract predictions and class probabilities from train objects
plot.varImp.train
Plotting variable importance measures
update.safs
Update or Re-fit a SA or GA Model
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
maxDissim
Maximum Dissimilarity Sampling
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
predict.gafs
Predict new samples
lift
Lift Plot
defaultSummary
Calculates performance across resamples
trainControl
Control parameters for train
resampleSummary
Summary of resampled performance estimates
resampleHist
Plot the resampling distribution of the model statistics
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
predict.knn3
Predictions from k-Nearest Neighbors
resamples
Collation and Visualization of Resampling Results
pottery
Pottery from Pre-Classical Sites in Italy
SLC14_1
Simulation Functions
update.train
Update or Re-fit a Model
rfe
Backwards Feature Selection
scat
Morphometric Data on Scat
segmentationData
Cell Body Segmentation
print.train
Print Method for the train Class
thresholder
Generate Data to Choose a Probability Threshold
prcomp.resamples
Principal Components Analysis of Resampling Results
recall
Calculate recall, precision and F values
train
Fit Predictive Models over Different Tuning Parameters
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
var_seq
Sequences of Variables for Tuning