caret v6.0-52


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by Max Kuhn

Classification and Regression Training

Misc functions for training and plotting classification and regression models.

Functions in caret

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