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caret (version 6.0-83)
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
6.0-83
License
GPL (>= 2)
Issues
173
Pull Requests
6
Stars
1,587
Forks
630
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
April 18th, 2019
Functions in caret (6.0-83)
Search functions
as.matrix.confusionMatrix
Confusion matrix as a table
densityplot.rfe
Lattice functions for plotting resampling results of recursive feature selection
downSample
Down- and Up-Sampling Imbalanced Data
index2vec
Convert indicies to a binary vector
dummyVars
Create A Full Set of Dummy Variables
knn3
k-Nearest Neighbour Classification
nullModel
Fit a simple, non-informative model
oil
Fatty acid composition of commercial oils
predict.gafs
Predict new samples
predict.knn3
Predictions from k-Nearest Neighbors
sbf
Selection By Filtering (SBF)
sbfControl
Control Object for Selection By Filtering (SBF)
trainControl
Control parameters for train
SLC14_1
Simulation Functions
GermanCredit
German Credit Data
caretSBF
Selection By Filtering (SBF) Helper Functions
bag
A General Framework For Bagging
bagEarth
Bagged Earth
classDist
Compute and predict the distances to class centroids
confusionMatrix
Create a confusion matrix
dotPlot
Create a dotplot of variable importance values
Sacramento
Sacramento CA Home Prices
knnreg
k-Nearest Neighbour Regression
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
dhfr
Dihydrofolate Reductase Inhibitors Data
cars
Kelly Blue Book resale data for 2005 model year GM cars
diff.resamples
Inferential Assessments About Model Performance
learning_curve_dat
Create Data to Plot a Learning Curve
lift
Lift Plot
caret-internal
Internal Functions
maxDissim
Maximum Dissimilarity Sampling
pickSizeBest
Backwards Feature Selection Helper Functions
histogram.train
Lattice functions for plotting resampling results
findCorrelation
Determine highly correlated variables
BloodBrain
Blood Brain Barrier Data
BoxCoxTrans
Box-Cox and Exponential Transformations
cox2
COX-2 Activity Data
confusionMatrix.train
Estimate a Resampled Confusion Matrix
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
filterVarImp
Calculation of filter-based variable importance
icr.formula
Independent Component Regression
findLinearCombos
Determine linear combinations in a matrix
oneSE
Selecting tuning Parameters
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
defaultSummary
Calculates performance across resamples
panel.lift2
Lattice Panel Functions for Lift Plots
pottery
Pottery from Pre-Classical Sites in Italy
gafs_initial
Ancillary genetic algorithm functions
bagFDA
Bagged FDA
prcomp.resamples
Principal Components Analysis of Resampling Results
train_model_list
A List of Available Models in train
getSamplingInfo
Get sampling info from a train model
plot.gafs
Plot Method for the gafs and safs Classes
nearZeroVar
Identification of near zero variance predictors
ggplot.rfe
Plot RFE Performance Profiles
print.train
Print Method for the train Class
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
ggplot.train
Plot Method for the train Class
modelLookup
Tools for Models Available in
train
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
thresholder
Generate Data to Choose a Probability Threshold
recall
Calculate recall, precision and F values
train
Fit Predictive Models over Different Tuning Parameters
varImp
Calculation of variable importance for regression and classification models
plot.varImp.train
Plotting variable importance measures
extractPrediction
Extract predictions and class probabilities from train objects
rfeControl
Controlling the Feature Selection Algorithms
panel.needle
Needle Plot Lattice Panel
varImp.gafs
Variable importances for GAs and SAs
pcaNNet
Neural Networks with a Principal Component Step
preProcess
Pre-Processing of Predictors
predict.bagEarth
Predicted values based on bagged Earth and FDA models
safs
Simulated annealing feature selection
calibration
Probability Calibration Plot
format.bagEarth
Format 'bagEarth' objects
scat
Morphometric Data on Scat
gafsControl
Control parameters for GA and SA feature selection
resamples
Collation and Visualization of Resampling Results
segmentationData
Cell Body Segmentation
update.safs
Update or Re-fit a SA or GA Model
safs_initial
Ancillary simulated annealing functions
rfe
Backwards Feature Selection
update.train
Update or Re-fit a Model
summary.bagEarth
Summarize a bagged earth or FDA fit
tecator
Fat, Water and Protein Content of Meat Samples
var_seq
Sequences of Variables for Tuning
gafs.default
Genetic algorithm feature selection
plotClassProbs
Plot Predicted Probabilities in Classification Models
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
predictors
List predictors used in the model
print.confusionMatrix
Print method for confusionMatrix
resampleHist
Plot the resampling distribution of the model statistics
resampleSummary
Summary of resampled performance estimates
negPredValue
Calculate sensitivity, specificity and predictive values
spatialSign
Compute the multivariate spatial sign
createDataPartition
Data Splitting functions
avNNet
Neural Networks Using Model Averaging