caret v6.0-37


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