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caret (version 6.0-85)
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
214,321
Version
6.0-85
License
GPL (>= 2)
Issues
180
Pull Requests
4
Stars
1,623
Forks
630
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
January 7th, 2020
Functions in caret (6.0-85)
Search all functions
as.matrix.confusionMatrix
Confusion matrix as a table
GermanCredit
German Credit Data
avNNet
Neural Networks Using Model Averaging
pickSizeBest
Backwards Feature Selection Helper Functions
caret-internal
Internal Functions
createDataPartition
Data Splitting functions
densityplot.rfe
Lattice functions for plotting resampling results of recursive feature selection
classDist
Compute and predict the distances to class centroids
confusionMatrix
Create a confusion matrix
Sacramento
Sacramento CA Home Prices
findCorrelation
Determine highly correlated variables
cars
Kelly Blue Book resale data for 2005 model year GM cars
caretSBF
Selection By Filtering (SBF) Helper Functions
dhfr
Dihydrofolate Reductase Inhibitors Data
dotPlot
Create a dotplot of variable importance values
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
diff.resamples
Inferential Assessments About Model Performance
findLinearCombos
Determine linear combinations in a matrix
knnreg
k-Nearest Neighbour Regression
icr.formula
Independent Component Regression
learning_curve_dat
Create Data to Plot a Learning Curve
filterVarImp
Calculation of filter-based variable importance
histogram.train
Lattice functions for plotting resampling results
gafs.default
Genetic algorithm feature selection
format.bagEarth
Format 'bagEarth' objects
index2vec
Convert indicies to a binary vector
cox2
COX-2 Activity Data
confusionMatrix.train
Estimate a Resampled Confusion Matrix
knn3
k-Nearest Neighbour Classification
getSamplingInfo
Get sampling info from a train model
downSample
Down- and Up-Sampling Imbalanced Data
gafs_initial
Ancillary genetic algorithm functions
dummyVars
Create A Full Set of Dummy Variables
nullModel
Fit a simple, non-informative model
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
oneSE
Selecting tuning Parameters
panel.lift2
Lattice Panel Functions for Lift Plots
modelLookup
Tools for Models Available in
train
nearZeroVar
Identification of near zero variance predictors
train_model_list
A List of Available Models in train
maxDissim
Maximum Dissimilarity Sampling
lift
Lift Plot
oil
Fatty acid composition of commercial oils
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
plot.gafs
Plot Method for the gafs and safs Classes
pcaNNet
Neural Networks with a Principal Component Step
panel.needle
Needle Plot Lattice Panel
plotClassProbs
Plot Predicted Probabilities in Classification Models
ggplot.train
Plot Method for the train Class
ggplot.rfe
Plot RFE Performance Profiles
defaultSummary
Calculates performance across resamples
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
pottery
Pottery from Pre-Classical Sites in Italy
prcomp.resamples
Principal Components Analysis of Resampling Results
print.confusionMatrix
Print method for confusionMatrix
predictors
List predictors used in the model
extractPrediction
Extract predictions and class probabilities from train objects
plot.varImp.train
Plotting variable importance measures
predict.gafs
Predict new samples
predict.knn3
Predictions from k-Nearest Neighbors
resampleHist
Plot the resampling distribution of the model statistics
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
resampleSummary
Summary of resampled performance estimates
rfe
Backwards Feature Selection
resamples
Collation and Visualization of Resampling Results
gafsControl
Control parameters for GA and SA feature selection
sbf
Selection By Filtering (SBF)
spatialSign
Compute the multivariate spatial sign
negPredValue
Calculate sensitivity, specificity and predictive values
rfeControl
Controlling the Feature Selection Algorithms
scat
Morphometric Data on Scat
segmentationData
Cell Body Segmentation
sbfControl
Control Object for Selection By Filtering (SBF)
safs
Simulated annealing feature selection
predict.bagEarth
Predicted values based on bagged Earth and FDA models
preProcess
Pre-Processing of Predictors
update.train
Update or Re-fit a Model
update.safs
Update or Re-fit a SA or GA Model
print.train
Print Method for the train Class
varImp
Calculation of variable importance for regression and classification models
recall
Calculate recall, precision and F values
trainControl
Control parameters for train
safs_initial
Ancillary simulated annealing functions
summary.bagEarth
Summarize a bagged earth or FDA fit
tecator
Fat, Water and Protein Content of Meat Samples
train
Fit Predictive Models over Different Tuning Parameters
SLC14_1
Simulation Functions
var_seq
Sequences of Variables for Tuning
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
thresholder
Generate Data to Choose a Probability Threshold
varImp.gafs
Variable importances for GAs and SAs
bag
A General Framework For Bagging
BloodBrain
Blood Brain Barrier Data
BoxCoxTrans
Box-Cox and Exponential Transformations
bagFDA
Bagged FDA
calibration
Probability Calibration Plot
bagEarth
Bagged Earth