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caret (version 6.0-84)
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
6.0-84
License
GPL (>= 2)
Issues
178
Pull Requests
7
Stars
1,615
Forks
632
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
April 27th, 2019
Functions in caret (6.0-84)
Search all functions
confusionMatrix.train
Estimate a Resampled Confusion Matrix
gafs_initial
Ancillary genetic algorithm functions
BloodBrain
Blood Brain Barrier Data
getSamplingInfo
Get sampling info from a train model
densityplot.rfe
Lattice functions for plotting resampling results of recursive feature selection
createDataPartition
Data Splitting functions
panel.lift2
Lattice Panel Functions for Lift Plots
oneSE
Selecting tuning Parameters
findLinearCombos
Determine linear combinations in a matrix
findCorrelation
Determine highly correlated variables
cox2
COX-2 Activity Data
dotPlot
Create a dotplot of variable importance values
knnreg
k-Nearest Neighbour Regression
BoxCoxTrans
Box-Cox and Exponential Transformations
dhfr
Dihydrofolate Reductase Inhibitors Data
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
pickSizeBest
Backwards Feature Selection Helper Functions
Sacramento
Sacramento CA Home Prices
diff.resamples
Inferential Assessments About Model Performance
caret-internal
Internal Functions
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
modelLookup
Tools for Models Available in
train
pottery
Pottery from Pre-Classical Sites in Italy
index2vec
Convert indicies to a binary vector
caretSBF
Selection By Filtering (SBF) Helper Functions
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
prcomp.resamples
Principal Components Analysis of Resampling Results
summary.bagEarth
Summarize a bagged earth or FDA fit
filterVarImp
Calculation of filter-based variable importance
learning_curve_dat
Create Data to Plot a Learning Curve
histogram.train
Lattice functions for plotting resampling results
icr.formula
Independent Component Regression
lift
Lift Plot
tecator
Fat, Water and Protein Content of Meat Samples
cars
Kelly Blue Book resale data for 2005 model year GM cars
knn3
k-Nearest Neighbour Classification
ggplot.train
Plot Method for the train Class
defaultSummary
Calculates performance across resamples
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
update.safs
Update or Re-fit a SA or GA Model
maxDissim
Maximum Dissimilarity Sampling
downSample
Down- and Up-Sampling Imbalanced Data
plot.varImp.train
Plotting variable importance measures
preProcess
Pre-Processing of Predictors
predict.gafs
Predict new samples
dummyVars
Create A Full Set of Dummy Variables
predict.bagEarth
Predicted values based on bagged Earth and FDA models
format.bagEarth
Format 'bagEarth' objects
gafs.default
Genetic algorithm feature selection
train_model_list
A List of Available Models in train
nearZeroVar
Identification of near zero variance predictors
update.train
Update or Re-fit a Model
predict.knn3
Predictions from k-Nearest Neighbors
nullModel
Fit a simple, non-informative model
oil
Fatty acid composition of commercial oils
panel.needle
Needle Plot Lattice Panel
print.train
Print Method for the train Class
plotClassProbs
Plot Predicted Probabilities in Classification Models
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
gafsControl
Control parameters for GA and SA feature selection
pcaNNet
Neural Networks with a Principal Component Step
safs_initial
Ancillary simulated annealing functions
scat
Morphometric Data on Scat
plot.gafs
Plot Method for the gafs and safs Classes
ggplot.rfe
Plot RFE Performance Profiles
segmentationData
Cell Body Segmentation
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
trainControl
Control parameters for train
extractPrediction
Extract predictions and class probabilities from train objects
recall
Calculate recall, precision and F values
resamples
Collation and Visualization of Resampling Results
predictors
List predictors used in the model
varImp
Calculation of variable importance for regression and classification models
SLC14_1
Simulation Functions
resampleHist
Plot the resampling distribution of the model statistics
resampleSummary
Summary of resampled performance estimates
sbf
Selection By Filtering (SBF)
rfe
Backwards Feature Selection
print.confusionMatrix
Print method for confusionMatrix
negPredValue
Calculate sensitivity, specificity and predictive values
varImp.gafs
Variable importances for GAs and SAs
spatialSign
Compute the multivariate spatial sign
rfeControl
Controlling the Feature Selection Algorithms
sbfControl
Control Object for Selection By Filtering (SBF)
safs
Simulated annealing feature selection
var_seq
Sequences of Variables for Tuning
thresholder
Generate Data to Choose a Probability Threshold
train
Fit Predictive Models over Different Tuning Parameters
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
as.matrix.confusionMatrix
Confusion matrix as a table
GermanCredit
German Credit Data
bag
A General Framework For Bagging
bagEarth
Bagged Earth
classDist
Compute and predict the distances to class centroids
confusionMatrix
Create a confusion matrix
avNNet
Neural Networks Using Model Averaging
bagFDA
Bagged FDA
calibration
Probability Calibration Plot