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 5.15-048)
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
160,352
Version
5.15-048
License
GPL-2
Maintainer
Max Kuhn
Last Published
December 21st, 2012
Functions in caret (5.15-048)
Search functions
downSample
Down- and Up-Sampling Imbalanced Data
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
confusionMatrix
Create a confusion matrix
normalize2Reference
Quantile Normalize Columns of a Matrix Based on a Reference Distribution
knnreg
k-Nearest Neighbour Regression
caret-internal
Internal Functions
prcomp.resamples
Principal Components Analysis of Resampling Results
pottery
Pottery from Pre-Classical Sites in Italy
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
format.bagEarth
Format 'bagEarth' objects
predict.knn3
Predictions from k-Nearest Neighbors
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
histogram.train
Lattice functions for plotting resampling results
predict.bagEarth
Predicted values based on bagged Earth and FDA models
maxDissim
Maximum Dissimilarity Sampling
predictors
List predictors used in the model
as.table.confusionMatrix
Save Confusion Table Results
findCorrelation
Determine highly correlated variables
bag.default
A General Framework For Bagging
plot.varImp.train
Plotting variable importance measures
oil
Fatty acid composition of commercial oils
caretSBF
Selection By Filtering (SBF) Helper Functions
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
createDataPartition
Data Splitting functions
diff.resamples
Inferential Assessments About Model Performance
nearZeroVar
Identification of near zero variance predictors
rfeControl
Controlling the Feature Selection Algorithms
bagFDA
Bagged FDA
BloodBrain
Blood Brain Barrier Data
spatialSign
Compute the multivariate spatial sign
plot.train
Plot Method for the train Class
postResample
Calculates performance across resamples
caretFuncs
Backwards Feature Selection Helper Functions
panel.lift2
Lattice Panel Functions for Lift Plots
modelLookup
Descriptions Of Models Available in train()
summary.bagEarth
Summarize a bagged earth or FDA fit
varImp
Calculation of variable importance for regression and classification models
icr.formula
Independent Component Regression
sbfControl
Control Object for Selection By Filtering (SBF)
resampleSummary
Summary of resampled performance estimates
print.train
Print Method for the train Class
print.confusionMatrix
Print method for confusionMatrix
sensitivity
Calculate sensitivity, specificity and predictive values
segmentationData
Cell Body Segmentation
preProcess
Pre-Processing of Predictors
avNNet.default
Neural Networks Using Model Averaging
rfe
Backwards Feature Selection
trainControl
Control parameters for train
filterVarImp
Calculation of filter-based variable importance
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
findLinearCombos
Determine linear combinations in a matrix
GermanCredit
German Credit Data
calibration
Probability Calibration Plot
nullModel
Fit a simple, non-informative model
BoxCoxTrans.default
Box-Cox Transformations
classDist
Compute and predict the distances to class centroids
cars
Kelly Blue Book resale data for 2005 model year GM cars
cox2
COX-2 Activity Data
confusionMatrix.train
Estimate a Resampled Confusion Matrix
pcaNNet.default
Neural Networks with a Principal Component Step
sbf
Selection By Filtering (SBF)
bagEarth
Bagged Earth
createGrid
Tuning Parameter Grid
panel.needle
Needle Plot Lattice Panel
dummyVars
Create A Full Set of Dummy Variables
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
predict.train
Extract predictions and class probabilities from train objects
knn3
k-Nearest Neighbour Classification
plotClassProbs
Plot Predicted Probabilities in Classification Models
resamples
Collation and Visualization of Resampling Results
oneSE
Selecting tuning Parameters
dhfr
Dihydrofolate Reductase Inhibitors Data
update.train
Update and Re-fit a Model
dotPlot
Create a dotplot of variable importance values
lift
Lift Plot
Alternate Affy Gene Expression Summary Methods.
Generate Expression Values from Probes
normalize.AffyBatch.normalize2Reference
Quantile Normalization to a Reference Distribution
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
resampleHist
Plot the resampling distribution of the model statistics
tecator
Fat, Water and Protein Content of Meat Samples