Rdocumentation
powered by
Learn R Programming
⚠️
There's a newer version (7.0-1) of this package.
Take me there.
caret (version 6.0-41)
Classification and Regression Training
Description
Misc functions for training and plotting classification and regression models
Copy Link
Link to current version
Version
Version
7.0-1
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
163,965
Version
6.0-41
License
GPL (>= 2)
Maintainer
Max Kuhn
Last Published
January 3rd, 2015
Functions in caret (6.0-41)
Search all functions
createDataPartition
Data Splitting functions
icr.formula
Independent Component Regression
format.bagEarth
Format 'bagEarth' objects
panel.needle
Needle Plot Lattice Panel
calibration
Probability Calibration Plot
dotPlot
Create a dotplot of variable importance values
avNNet.default
Neural Networks Using Model Averaging
classDist
Compute and predict the distances to class centroids
GermanCredit
German Credit Data
BloodBrain
Blood Brain Barrier Data
cars
Kelly Blue Book resale data for 2005 model year GM cars
caret-internal
Internal Functions
trainControl
Control parameters for train
histogram.train
Lattice functions for plotting resampling results
findCorrelation
Determine highly correlated variables
bagEarth
Bagged Earth
BoxCoxTrans.default
Box-Cox and Exponential Transformations
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
bagFDA
Bagged FDA
confusionMatrix
Create a confusion matrix
maxDissim
Maximum Dissimilarity Sampling
findLinearCombos
Determine linear combinations in a matrix
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
dummyVars
Create A Full Set of Dummy Variables
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
predict.train
Extract predictions and class probabilities from train objects
print.confusionMatrix
Print method for confusionMatrix
rfeControl
Controlling the Feature Selection Algorithms
nullModel
Fit a simple, non-informative model
downSample
Down- and Up-Sampling Imbalanced Data
as.table.confusionMatrix
Save Confusion Table Results
pcaNNet.default
Neural Networks with a Principal Component Step
plot.train
Plot Method for the train Class
sbf
Selection By Filtering (SBF)
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
dhfr
Dihydrofolate Reductase Inhibitors Data
predictors
List predictors used in the model
index2vec
Convert indicies to a binary vector
plot.gafs
Plot Method for the gafs and safs Classes
confusionMatrix.train
Estimate a Resampled Confusion Matrix
preProcess
Pre-Processing of Predictors
diff.resamples
Inferential Assessments About Model Performance
train_model_list
A List of Available Models in train
gafs.default
Genetic algorithm feature selection
knnreg
k-Nearest Neighbour Regression
resamples
Collation and Visualization of Resampling Results
pottery
Pottery from Pre-Classical Sites in Italy
twoClassSim
Simulation Functions
update.train
Update or Re-fit a Model
bag.default
A General Framework For Bagging
knn3
k-Nearest Neighbour Classification
safs.default
Simulated annealing feature selection
cox2
COX-2 Activity Data
gafs_initial
Ancillary genetic algorithm functions
filterVarImp
Calculation of filter-based variable importance
prcomp.resamples
Principal Components Analysis of Resampling Results
lift
Lift Plot
tecator
Fat, Water and Protein Content of Meat Samples
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
resampleSummary
Summary of resampled performance estimates
postResample
Calculates performance across resamples
panel.lift2
Lattice Panel Functions for Lift Plots
plotClassProbs
Plot Predicted Probabilities in Classification Models
varImp.gafs
Variable importances for GAs and SAs
plot.varImp.train
Plotting variable importance measures
modelLookup
Tools for Models Available in
train
oil
Fatty acid composition of commercial oils
plot.rfe
Plot RFE Performance Profiles
nearZeroVar
Identification of near zero variance predictors
resampleHist
Plot the resampling distribution of the model statistics
safs_initial
Ancillary simulated annealing functions
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
segmentationData
Cell Body Segmentation
caretFuncs
Backwards Feature Selection Helper Functions
sensitivity
Calculate sensitivity, specificity and predictive values
spatialSign
Compute the multivariate spatial sign
caretSBF
Selection By Filtering (SBF) Helper Functions
update.safs
Update or Re-fit a SA or GA Model
print.train
Print Method for the train Class
predict.knn3
Predictions from k-Nearest Neighbors
summary.bagEarth
Summarize a bagged earth or FDA fit
oneSE
Selecting tuning Parameters
rfe
Backwards Feature Selection
sbfControl
Control Object for Selection By Filtering (SBF)
predict.gafs
Predict new samples
varImp
Calculation of variable importance for regression and classification models
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
predict.bagEarth
Predicted values based on bagged Earth and FDA models
safsControl
Control parameters for GA and SA feature selection
train
Fit Predictive Models over Different Tuning Parameters