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caret (version 6.0-79)
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
230,598
Version
6.0-79
License
GPL (>= 2)
Issues
184
Pull Requests
4
Stars
1,645
Forks
629
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
March 29th, 2018
Functions in caret (6.0-79)
Search all functions
BloodBrain
Blood Brain Barrier Data
bag
A General Framework For Bagging
bagFDA
Bagged FDA
Sacramento
Sacramento CA Home Prices
GermanCredit
German Credit Data
BoxCoxTrans
Box-Cox and Exponential Transformations
bagEarth
Bagged Earth
avNNet
Neural Networks Using Model Averaging
classDist
Compute and predict the distances to class centroids
caretSBF
Selection By Filtering (SBF) Helper Functions
confusionMatrix.train
Estimate a Resampled Confusion Matrix
calibration
Probability Calibration Plot
pickSizeBest
Backwards Feature Selection Helper Functions
caret-internal
Internal Functions
as.matrix.confusionMatrix
Confusion matrix as a table
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
downSample
Down- and Up-Sampling Imbalanced Data
cox2
COX-2 Activity Data
filterVarImp
Calculation of filter-based variable importance
findLinearCombos
Determine linear combinations in a matrix
dotPlot
Create a dotplot of variable importance values
dhfr
Dihydrofolate Reductase Inhibitors Data
confusionMatrix
Create a confusion matrix
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
diff.resamples
Inferential Assessments About Model Performance
knn3
k-Nearest Neighbour Classification
createDataPartition
Data Splitting functions
index2vec
Convert indicies to a binary vector
gafs.default
Genetic algorithm feature selection
getSamplingInfo
Get sampling info from a train model
densityplot.rfe
Lattice functions for plotting resampling results of recursive feature selection
learing_curve_dat
Create Data to Plot a Learning Curve
cars
Kelly Blue Book resale data for 2005 model year GM cars
icr.formula
Independent Component Regression
findCorrelation
Determine highly correlated variables
format.bagEarth
Format 'bagEarth' objects
dummyVars
Create A Full Set of Dummy Variables
histogram.train
Lattice functions for plotting resampling results
gafs_initial
Ancillary genetic algorithm functions
knnreg
k-Nearest Neighbour Regression
lift
Lift Plot
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
oneSE
Selecting tuning Parameters
modelLookup
Tools for Models Available in
train
maxDissim
Maximum Dissimilarity Sampling
oil
Fatty acid composition of commercial oils
panel.lift2
Lattice Panel Functions for Lift Plots
train_model_list
A List of Available Models in train
nullModel
Fit a simple, non-informative model
plotClassProbs
Plot Predicted Probabilities in Classification Models
panel.needle
Needle Plot Lattice Panel
nearZeroVar
Identification of near zero variance predictors
defaultSummary
Calculates performance across resamples
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
ggplot.rfe
Plot RFE Performance Profiles
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
pcaNNet
Neural Networks with a Principal Component Step
plot.gafs
Plot Method for the gafs and safs Classes
ggplot.train
Plot Method for the train Class
print.confusionMatrix
Print method for confusionMatrix
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
predict.bagEarth
Predicted values based on bagged Earth and FDA models
preProcess
Pre-Processing of Predictors
plot.varImp.train
Plotting variable importance measures
predict.gafs
Predict new samples
extractPrediction
Extract predictions and class probabilities from train objects
prcomp.resamples
Principal Components Analysis of Resampling Results
predictors
List predictors used in the model
predict.knn3
Predictions from k-Nearest Neighbors
pottery
Pottery from Pre-Classical Sites in Italy
rfe
Backwards Feature Selection
safs_initial
Ancillary simulated annealing functions
safs
Simulated annealing feature selection
gafsControl
Control parameters for GA and SA feature selection
print.train
Print Method for the train Class
rfeControl
Controlling the Feature Selection Algorithms
recall
Calculate recall, precision and F values
resampleSummary
Summary of resampled performance estimates
resampleHist
Plot the resampling distribution of the model statistics
resamples
Collation and Visualization of Resampling Results
tecator
Fat, Water and Protein Content of Meat Samples
summary.bagEarth
Summarize a bagged earth or FDA fit
sbf
Selection By Filtering (SBF)
sbfControl
Control Object for Selection By Filtering (SBF)
thresholder
Generate Data to Choose a Probability Threshold
segmentationData
Cell Body Segmentation
train
Fit Predictive Models over Different Tuning Parameters
negPredValue
Calculate sensitivity, specificity and predictive values
scat
Morphometric Data on Scat
varImp
Calculation of variable importance for regression and classification models
spatialSign
Compute the multivariate spatial sign
update.safs
Update or Re-fit a SA or GA Model
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
varImp.gafs
Variable importances for GAs and SAs
update.train
Update or Re-fit a Model
var_seq
Sequences of Variables for Tuning
trainControl
Control parameters for train
SLC14_1
Simulation Functions