caret v6.0-35

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