caret v6.0-64

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