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caret (version 6.0-78)
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
208,749
Version
6.0-78
License
GPL (>= 2)
Issues
178
Pull Requests
7
Stars
1,614
Forks
632
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
December 10th, 2017
Functions in caret (6.0-78)
Search all functions
bag
A General Framework For Bagging
GermanCredit
German Credit Data
bagEarth
Bagged Earth
Sacramento
Sacramento CA Home Prices
as.matrix.confusionMatrix
Confusion matrix as a table
BloodBrain
Blood Brain Barrier Data
bagFDA
Bagged FDA
createDataPartition
Data Splitting functions
calibration
Probability Calibration Plot
cars
Kelly Blue Book resale data for 2005 model year GM cars
classDist
Compute and predict the distances to class centroids
BoxCoxTrans
Box-Cox and Exponential Transformations
confusionMatrix
Create a confusion matrix
caretSBF
Selection By Filtering (SBF) Helper Functions
confusionMatrix.train
Estimate a Resampled Confusion Matrix
densityplot.rfe
Lattice functions for plotting resampling results of recursive feature selection
cox2
COX-2 Activity Data
pickSizeBest
Backwards Feature Selection Helper Functions
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
filterVarImp
Calculation of filter-based variable importance
dotPlot
Create a dotplot of variable importance values
downSample
Down- and Up-Sampling Imbalanced Data
dhfr
Dihydrofolate Reductase Inhibitors Data
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
diff.resamples
Inferential Assessments About Model Performance
findCorrelation
Determine highly correlated variables
histogram.train
Lattice functions for plotting resampling results
findLinearCombos
Determine linear combinations in a matrix
icr.formula
Independent Component Regression
knnreg
k-Nearest Neighbour Regression
dummyVars
Create A Full Set of Dummy Variables
learing_curve_dat
Create Data to Plot a Learning Curve
caret-internal
Internal Functions
format.bagEarth
Format 'bagEarth' objects
gafs.default
Genetic algorithm feature selection
index2vec
Convert indicies to a binary vector
lift
Lift Plot
knn3
k-Nearest Neighbour Classification
nullModel
Fit a simple, non-informative model
gafs_initial
Ancillary genetic algorithm functions
oil
Fatty acid composition of commercial oils
nearZeroVar
Identification of near zero variance predictors
getSamplingInfo
Get sampling info from a train model
oneSE
Selecting tuning Parameters
modelLookup
Tools for Models Available in
train
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
panel.lift2
Lattice Panel Functions for Lift Plots
plot.varImp.train
Plotting variable importance measures
train_model_list
A List of Available Models in train
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
defaultSummary
Calculates performance across resamples
maxDissim
Maximum Dissimilarity Sampling
panel.needle
Needle Plot Lattice Panel
plotClassProbs
Plot Predicted Probabilities in Classification Models
pcaNNet
Neural Networks with a Principal Component Step
ggplot.rfe
Plot RFE Performance Profiles
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
pottery
Pottery from Pre-Classical Sites in Italy
plot.gafs
Plot Method for the gafs and safs Classes
prcomp.resamples
Principal Components Analysis of Resampling Results
ggplot.train
Plot Method for the train Class
predict.knn3
Predictions from k-Nearest Neighbors
resamples
Collation and Visualization of Resampling Results
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
rfe
Backwards Feature Selection
preProcess
Pre-Processing of Predictors
predict.bagEarth
Predicted values based on bagged Earth and FDA models
gafsControl
Control parameters for GA and SA feature selection
predict.gafs
Predict new samples
safs_initial
Ancillary simulated annealing functions
extractPrediction
Extract predictions and class probabilities from train objects
avNNet
Neural Networks Using Model Averaging
print.train
Print Method for the train Class
sbf
Selection By Filtering (SBF)
sbfControl
Control Object for Selection By Filtering (SBF)
recall
Calculate recall, precision and F values
scat
Morphometric Data on Scat
predictors
List predictors used in the model
segmentationData
Cell Body Segmentation
update.safs
Update or Re-fit a SA or GA Model
print.confusionMatrix
Print method for confusionMatrix
update.train
Update or Re-fit a Model
resampleHist
Plot the resampling distribution of the model statistics
negPredValue
Calculate sensitivity, specificity and predictive values
resampleSummary
Summary of resampled performance estimates
spatialSign
Compute the multivariate spatial sign
trainControl
Control parameters for train
summary.bagEarth
Summarize a bagged earth or FDA fit
SLC14_1
Simulation Functions
tecator
Fat, Water and Protein Content of Meat Samples
varImp
Calculation of variable importance for regression and classification models
rfeControl
Controlling the Feature Selection Algorithms
varImp.gafs
Variable importances for GAs and SAs
safs
Simulated annealing feature selection
thresholder
Generate Data to Choose a Probability Threshold
train
Fit Predictive Models over Different Tuning Parameters
var_seq
Sequences of Variables for Tuning
xyplot.resamples
Lattice Functions for Visualizing Resampling Results