RDocumentation
Moon
Learn R
Search all packages and functions
⚠️
There's a newer version (6.0-94) of this package.
Take me there.
caret (version 6.0-64)
Classification and Regression Training
Description
Misc functions for training and plotting classification and regression models.
Copy Link
Copy
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
Down Chevron
Install
install.packages('caret')
Monthly Downloads
184,121
Version
6.0-64
License
GPL (>= 2)
Issues
175
Pull Requests
7
Stars
1,590
Forks
631
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
January 6th, 2016
Functions in caret (6.0-64)
Search functions
cars
Kelly Blue Book resale data for 2005 model year GM cars
caret-internal
Internal Functions
GermanCredit
German Credit Data
prcomp.resamples
Principal Components Analysis of Resampling Results
filterVarImp
Calculation of filter-based variable importance
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
cox2
COX-2 Activity Data
panel.needle
Needle Plot Lattice Panel
BoxCoxTrans.default
Box-Cox and Exponential Transformations
bag.default
A General Framework For Bagging
icr.formula
Independent Component Regression
sbfControl
Control Object for Selection By Filtering (SBF)
classDist
Compute and predict the distances to class centroids
oil
Fatty acid composition of commercial oils
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
twoClassSim
Simulation Functions
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
varImp.gafs
Variable importances for GAs and SAs
print.confusionMatrix
Print method for confusionMatrix
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
findCorrelation
Determine highly correlated variables
modelLookup
Tools for Models Available in
train
confusionMatrix.train
Estimate a Resampled Confusion Matrix
format.bagEarth
Format 'bagEarth' objects
tecator
Fat, Water and Protein Content of Meat Samples
bagEarth
Bagged Earth
dummyVars
Create A Full Set of Dummy Variables
index2vec
Convert indicies to a binary vector
predict.knn3
Predictions from k-Nearest Neighbors
downSample
Down- and Up-Sampling Imbalanced Data
resampleHist
Plot the resampling distribution of the model statistics
pcaNNet.default
Neural Networks with a Principal Component Step
pottery
Pottery from Pre-Classical Sites in Italy
nullModel
Fit a simple, non-informative model
train_model_list
A List of Available Models in train
caretFuncs
Backwards Feature Selection Helper Functions
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
as.table.confusionMatrix
Save Confusion Table Results
avNNet.default
Neural Networks Using Model Averaging
trainControl
Control parameters for train
plot.varImp.train
Plotting variable importance measures
gafs.default
Genetic algorithm feature selection
safs.default
Simulated annealing feature selection
dotPlot
Create a dotplot of variable importance values
plot.gafs
Plot Method for the gafs and safs Classes
var_seq
Sequences of Variables for Tuning
safsControl
Control parameters for GA and SA feature selection
histogram.train
Lattice functions for plotting resampling results
maxDissim
Maximum Dissimilarity Sampling
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
BloodBrain
Blood Brain Barrier Data
dhfr
Dihydrofolate Reductase Inhibitors Data
resamples
Collation and Visualization of Resampling Results
confusionMatrix
Create a confusion matrix
update.safs
Update or Re-fit a SA or GA Model
bagFDA
Bagged FDA
predict.train
Extract predictions and class probabilities from train objects
predict.gafs
Predict new samples
panel.lift2
Lattice Panel Functions for Lift Plots
nearZeroVar
Identification of near zero variance predictors
rfeControl
Controlling the Feature Selection Algorithms
learing_curve_dat
Create Data to Plot a Learning Curve
getSamplingInfo
Get sampling info from a train model
plot.train
Plot Method for the train Class
sbf
Selection By Filtering (SBF)
predict.bagEarth
Predicted values based on bagged Earth and FDA models
predictors
List predictors used in the model
spatialSign
Compute the multivariate spatial sign
print.train
Print Method for the train Class
caretSBF
Selection By Filtering (SBF) Helper Functions
oneSE
Selecting tuning Parameters
segmentationData
Cell Body Segmentation
summary.bagEarth
Summarize a bagged earth or FDA fit
sensitivity
Calculate sensitivity, specificity and predictive values
plotClassProbs
Plot Predicted Probabilities in Classification Models
varImp
Calculation of variable importance for regression and classification models
safs_initial
Ancillary simulated annealing functions
preProcess
Pre-Processing of Predictors
gafs_initial
Ancillary genetic algorithm functions
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
knnreg
k-Nearest Neighbour Regression
diff.resamples
Inferential Assessments About Model Performance
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
resampleSummary
Summary of resampled performance estimates
postResample
Calculates performance across resamples
knn3
k-Nearest Neighbour Classification
plot.rfe
Plot RFE Performance Profiles
createDataPartition
Data Splitting functions
lift
Lift Plot
update.train
Update or Re-fit a Model
findLinearCombos
Determine linear combinations in a matrix
rfe
Backwards Feature Selection
calibration
Probability Calibration Plot
train
Fit Predictive Models over Different Tuning Parameters