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caret (version 6.0-47)
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
163,965
Version
6.0-47
License
GPL (>= 2)
Maintainer
Max Kuhn
Last Published
May 6th, 2015
Functions in caret (6.0-47)
Search all functions
BoxCoxTrans.default
Box-Cox and Exponential Transformations
findLinearCombos
Determine linear combinations in a matrix
GermanCredit
German Credit Data
dotplot.diff.resamples
Lattice Functions for Visualizing Resampling Differences
cox2
COX-2 Activity Data
downSample
Down- and Up-Sampling Imbalanced Data
caret-internal
Internal Functions
bagFDA
Bagged FDA
createDataPartition
Data Splitting functions
plotClassProbs
Plot Predicted Probabilities in Classification Models
BloodBrain
Blood Brain Barrier Data
knnreg
k-Nearest Neighbour Regression
trainControl
Control parameters for train
predict.gafs
Predict new samples
bagEarth
Bagged Earth
bag.default
A General Framework For Bagging
rfe
Backwards Feature Selection
knn3
k-Nearest Neighbour Classification
resampleSummary
Summary of resampled performance estimates
plot.train
Plot Method for the train Class
lattice.rfe
Lattice functions for plotting resampling results of recursive feature selection
confusionMatrix.train
Estimate a Resampled Confusion Matrix
sbf
Selection By Filtering (SBF)
classDist
Compute and predict the distances to class centroids
nearZeroVar
Identification of near zero variance predictors
update.train
Update or Re-fit a Model
predict.knn3
Predictions from k-Nearest Neighbors
xyplot.resamples
Lattice Functions for Visualizing Resampling Results
pottery
Pottery from Pre-Classical Sites in Italy
format.bagEarth
Format 'bagEarth' objects
nullModel
Fit a simple, non-informative model
var_seq
Sequences of Variables for Tuning
dotPlot
Create a dotplot of variable importance values
postResample
Calculates performance across resamples
panel.lift2
Lattice Panel Functions for Lift Plots
print.confusionMatrix
Print method for confusionMatrix
dummyVars
Create A Full Set of Dummy Variables
segmentationData
Cell Body Segmentation
safs_initial
Ancillary simulated annealing functions
lift
Lift Plot
spatialSign
Compute the multivariate spatial sign
predictors
List predictors used in the model
resampleHist
Plot the resampling distribution of the model statistics
predict.bagEarth
Predicted values based on bagged Earth and FDA models
update.safs
Update or Re-fit a SA or GA Model
caretSBF
Selection By Filtering (SBF) Helper Functions
avNNet.default
Neural Networks Using Model Averaging
gafs_initial
Ancillary genetic algorithm functions
twoClassSim
Simulation Functions
oneSE
Selecting tuning Parameters
as.table.confusionMatrix
Save Confusion Table Results
calibration
Probability Calibration Plot
confusionMatrix
Create a confusion matrix
cars
Kelly Blue Book resale data for 2005 model year GM cars
icr.formula
Independent Component Regression
plsda
Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
caretFuncs
Backwards Feature Selection Helper Functions
safsControl
Control parameters for GA and SA feature selection
plot.rfe
Plot RFE Performance Profiles
predict.knnreg
Predictions from k-Nearest Neighbors Regression Model
histogram.train
Lattice functions for plotting resampling results
plot.gafs
Plot Method for the gafs and safs Classes
print.train
Print Method for the train Class
varImp
Calculation of variable importance for regression and classification models
dhfr
Dihydrofolate Reductase Inhibitors Data
index2vec
Convert indicies to a binary vector
oil
Fatty acid composition of commercial oils
predict.train
Extract predictions and class probabilities from train objects
train_model_list
A List of Available Models in train
safs.default
Simulated annealing feature selection
pcaNNet.default
Neural Networks with a Principal Component Step
panel.needle
Needle Plot Lattice Panel
findCorrelation
Determine highly correlated variables
tecator
Fat, Water and Protein Content of Meat Samples
sbfControl
Control Object for Selection By Filtering (SBF)
plotObsVsPred
Plot Observed versus Predicted Results in Regression and Classification Models
mdrr
Multidrug Resistance Reversal (MDRR) Agent Data
maxDissim
Maximum Dissimilarity Sampling
prcomp.resamples
Principal Components Analysis of Resampling Results
varImp.gafs
Variable importances for GAs and SAs
preProcess
Pre-Processing of Predictors
modelLookup
Tools for Models Available in
train
diff.resamples
Inferential Assessments About Model Performance
filterVarImp
Calculation of filter-based variable importance
plot.varImp.train
Plotting variable importance measures
featurePlot
Wrapper for Lattice Plotting of Predictor Variables
summary.bagEarth
Summarize a bagged earth or FDA fit
gafs.default
Genetic algorithm feature selection
rfeControl
Controlling the Feature Selection Algorithms
sensitivity
Calculate sensitivity, specificity and predictive values
resamples
Collation and Visualization of Resampling Results
train
Fit Predictive Models over Different Tuning Parameters