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caret (version 6.0-80)
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
172,185
Version
6.0-80
License
GPL (>= 2)
Issues
153
Pull Requests
3
Stars
1,521
Forks
635
Repository
https://github.com/topepo/caret/
Maintainer
Max Kuhn
Last Published
May 26th, 2018
Functions in caret (6.0-80)
Search functions
cars
Kelly Blue Book resale data for 2005 model year GM cars
dhfr
Dihydrofolate Reductase Inhibitors Data
gafs_initial
Ancillary genetic algorithm functions
densityplot.rfe
Lattice functions for plotting resampling results of recursive feature selection
getSamplingInfo
Get sampling info from a train model
recall
Calculate recall, precision and F values
print.train
Print Method for the train Class
print.confusionMatrix
Print method for confusionMatrix
plot.varImp.train
Plotting variable importance measures
lift
Lift Plot
predictors
List predictors used in the model
findLinearCombos
Determine linear combinations in a matrix
maxDissim
Maximum Dissimilarity Sampling
findCorrelation
Determine highly correlated variables
histogram.train
Lattice functions for plotting resampling results
ggplot.train
Plot Method for the train Class
nearZeroVar
Identification of near zero variance predictors
icr.formula
Independent Component Regression
plot.gafs
Plot Method for the gafs and safs Classes
train_model_list
A List of Available Models in train
downSample
Down- and Up-Sampling Imbalanced Data
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
diff.resamples
Inferential Assessments About Model Performance
predict.gafs
Predict new samples
filterVarImp
Calculation of filter-based variable importance
thresholder
Generate Data to Choose a Probability Threshold
dummyVars
Create A Full Set of Dummy Variables
format.bagEarth
Format 'bagEarth' objects
gafs.default
Genetic algorithm feature selection
nullModel
Fit a simple, non-informative model
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
knnreg
k-Nearest Neighbour Regression
oil
Fatty acid composition of commercial oils
knn3
k-Nearest Neighbour Classification
dotPlot
Create a dotplot of variable importance values
learing_curve_dat
Create Data to Plot a Learning Curve
index2vec
Convert indicies to a binary vector
oneSE
Selecting tuning Parameters
train
Fit Predictive Models over Different Tuning Parameters
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
ggplot.rfe
Plot RFE Performance Profiles
modelLookup
Tools for Models Available in
train
pottery
Pottery from Pre-Classical Sites in Italy
plotClassProbs
Plot Predicted Probabilities in Classification Models
pcaNNet
Neural Networks with a Principal Component Step
preProcess
Pre-Processing of Predictors
predict.knn3
Predictions from k-Nearest Neighbors
varImp.gafs
Variable importances for GAs and SAs
predict.bagEarth
Predicted values based on bagged Earth and FDA models
panel.lift2
Lattice Panel Functions for Lift Plots
varImp
Calculation of variable importance for regression and classification models
panel.needle
Needle Plot Lattice Panel
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
summary.bagEarth
Summarize a bagged earth or FDA fit
resampleHist
Plot the resampling distribution of the model statistics
defaultSummary
Calculates performance across resamples
tecator
Fat, Water and Protein Content of Meat Samples
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
extractPrediction
Extract predictions and class probabilities from train objects
update.safs
Update or Re-fit a SA or GA Model
safs
Simulated annealing feature selection
rfeControl
Controlling the Feature Selection Algorithms
resamples
Collation and Visualization of Resampling Results
update.train
Update or Re-fit a Model
negPredValue
Calculate sensitivity, specificity and predictive values
prcomp.resamples
Principal Components Analysis of Resampling Results
resampleSummary
Summary of resampled performance estimates
segmentationData
Cell Body Segmentation
safs_initial
Ancillary simulated annealing functions
scat
Morphometric Data on Scat
gafsControl
Control parameters for GA and SA feature selection
spatialSign
Compute the multivariate spatial sign
rfe
Backwards Feature Selection
sbf
Selection By Filtering (SBF)
sbfControl
Control Object for Selection By Filtering (SBF)
var_seq
Sequences of Variables for Tuning
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
trainControl
Control parameters for train
SLC14_1
Simulation Functions
createDataPartition
Data Splitting functions
bag
A General Framework For Bagging
as.matrix.confusionMatrix
Confusion matrix as a table
bagFDA
Bagged FDA
avNNet
Neural Networks Using Model Averaging
classDist
Compute and predict the distances to class centroids
calibration
Probability Calibration Plot
caret-internal
Internal Functions
BloodBrain
Blood Brain Barrier Data
pickSizeBest
Backwards Feature Selection Helper Functions
confusionMatrix
Create a confusion matrix
BoxCoxTrans
Box-Cox and Exponential Transformations
bagEarth
Bagged Earth
caretSBF
Selection By Filtering (SBF) Helper Functions
GermanCredit
German Credit Data
Sacramento
Sacramento CA Home Prices
confusionMatrix.train
Estimate a Resampled Confusion Matrix
cox2
COX-2 Activity Data