caret v6.0-30


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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
format.bagEarth Format 'bagEarth' objects
cars Kelly Blue Book resale data for 2005 model year GM cars
bagEarth Bagged Earth
findCorrelation Determine highly correlated variables
confusionMatrix.train Estimate a Resampled Confusion Matrix
icr.formula Independent Component Regression
modelLookup Tools for Models Available in train
dotPlot Create a dotplot of variable importance values
panel.lift2 Lattice Panel Functions for Lift Plots
avNNet.default Neural Networks Using Model Averaging
bag.default A General Framework For Bagging
nullModel Fit a simple, non-informative model
preProcess Pre-Processing of Predictors
classDist Compute and predict the distances to class centroids
predict.knnreg Predictions from k-Nearest Neighbors Regression Model
xyplot.resamples Lattice Functions for Visualizing Resampling Results
dhfr Dihydrofolate Reductase Inhibitors Data
createDataPartition Data Splitting functions
GermanCredit German Credit Data
featurePlot Wrapper for Lattice Plotting of Predictor Variables
lattice.rfe Lattice functions for plotting resampling results of recursive feature selection
resampleHist Plot the resampling distribution of the model statistics
diff.resamples Inferential Assessments About Model Performance
filterVarImp Calculation of filter-based variable importance
caretFuncs Backwards Feature Selection Helper Functions
normalize2Reference Quantile Normalize Columns of a Matrix Based on a Reference Distribution
oil Fatty acid composition of commercial oils
mdrr Multidrug Resistance Reversal (MDRR) Agent Data
calibration Probability Calibration Plot
knnreg k-Nearest Neighbour Regression
nearZeroVar Identification of near zero variance predictors
trainControl Control parameters for train
plot.train Plot Method for the train Class
dummyVars Create A Full Set of Dummy Variables
findLinearCombos Determine linear combinations in a matrix
predict.bagEarth Predicted values based on bagged Earth and FDA models
plotClassProbs Plot Predicted Probabilities in Classification Models
twoClassSim Two-Class Simulations
BloodBrain Blood Brain Barrier Data
resampleSummary Summary of resampled performance estimates
predict.train Extract predictions and class probabilities from train objects
resamples Collation and Visualization of Resampling Results
rfeControl Controlling the Feature Selection Algorithms
histogram.train Lattice functions for plotting resampling results
pcaNNet.default Neural Networks with a Principal Component Step
rfe Backwards Feature Selection
bagFDA Bagged FDA
sbfControl Control Object for Selection By Filtering (SBF)
postResample Calculates performance across resamples
segmentationData Cell Body Segmentation
confusionMatrix Create a confusion matrix
lift Lift Plot
summary.bagEarth Summarize a bagged earth or FDA fit
varImp Calculation of variable importance for regression and classification models
predict.knn3 Predictions from k-Nearest Neighbors
caret-internal Internal Functions
panel.needle Needle Plot Lattice Panel
Alternate Affy Gene Expression Summary Methods. Generate Expression Values from Probes
plotObsVsPred Plot Observed versus Predicted Results in Regression and Classification Models
knn3 k-Nearest Neighbour Classification
print.train Print Method for the train Class
plot.rfe Plot RFE Performance Profiles
train_model_list A List of Available Models in train
prcomp.resamples Principal Components Analysis of Resampling Results
plot.varImp.train Plotting variable importance measures
print.confusionMatrix Print method for confusionMatrix
pottery Pottery from Pre-Classical Sites in Italy
predictors List predictors used in the model
plsda Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
sbf Selection By Filtering (SBF)
caretSBF Selection By Filtering (SBF) Helper Functions
normalize.AffyBatch.normalize2Reference Quantile Normalization to a Reference Distribution
cox2 COX-2 Activity Data
as.table.confusionMatrix Save Confusion Table Results
oneSE Selecting tuning Parameters
tecator Fat, Water and Protein Content of Meat Samples
spatialSign Compute the multivariate spatial sign
downSample Down- and Up-Sampling Imbalanced Data
dotplot.diff.resamples Lattice Functions for Visualizing Resampling Differences
maxDissim Maximum Dissimilarity Sampling
update.train Update or Re-fit a Model
sensitivity Calculate sensitivity, specificity and predictive values
BoxCoxTrans.default Box-Cox and Exponential Transformations
train Fit Predictive Models over Different Tuning Parameters
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