caret v6.0-57


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