caret v6.0-68


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