caret v6.0-73


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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 A General Framework For Bagging
pickSizeBest Backwards Feature Selection Helper Functions
caretSBF Selection By Filtering (SBF) Helper Functions
bagFDA Bagged FDA
BoxCoxTrans Box-Cox and Exponential Transformations
calibration Probability Calibration Plot
bagEarth Bagged Earth
caret-internal Internal Functions
downSample Down- and Up-Sampling Imbalanced Data
confusionMatrix.train Estimate a Resampled Confusion Matrix
featurePlot Wrapper for Lattice Plotting of Predictor Variables
createDataPartition Data Splitting functions
dotPlot Create a dotplot of variable importance values
densityplot.rfe Lattice functions for plotting resampling results of recursive feature selection
dotplot.diff.resamples Lattice Functions for Visualizing Resampling Differences
dummyVars Create A Full Set of Dummy Variables
classDist Compute and predict the distances to class centroids
diff.resamples Inferential Assessments About Model Performance
GermanCredit German Credit Data
findLinearCombos Determine linear combinations in a matrix
gafs_initial Ancillary genetic algorithm functions
getSamplingInfo Get sampling info from a train model
gafs.default Genetic algorithm feature selection
format.bagEarth Format 'bagEarth' objects
filterVarImp Calculation of filter-based variable importance
findCorrelation Determine highly correlated variables
histogram.train Lattice functions for plotting resampling results
icr.formula Independent Component Regression
lift Lift Plot
maxDissim Maximum Dissimilarity Sampling
learing_curve_dat Create Data to Plot a Learning Curve
knn3 k-Nearest Neighbour Classification
modelLookup Tools for Models Available in train
nullModel Fit a simple, non-informative model
nearZeroVar Identification of near zero variance predictors
index2vec Convert indicies to a binary vector
knnreg k-Nearest Neighbour Regression
pcaNNet Neural Networks with a Principal Component Step
train_model_list A List of Available Models in train
plotObsVsPred Plot Observed versus Predicted Results in Regression and Classification Models
panel.lift2 Lattice Panel Functions for Lift Plots
plot.varImp.train Plotting variable importance measures
panel.needle Needle Plot Lattice Panel
plotClassProbs Plot Predicted Probabilities in Classification Models
ggplot.train Plot Method for the train Class
plot.gafs Plot Method for the gafs and safs Classes
ggplot.rfe Plot RFE Performance Profiles
preProcess Pre-Processing of Predictors
oneSE Selecting tuning Parameters
print.confusionMatrix Print method for confusionMatrix
extractPrediction Extract predictions and class probabilities from train objects
predict.bagEarth Predicted values based on bagged Earth and FDA models
predictors List predictors used in the model
predict.knn3 Predictions from k-Nearest Neighbors
predict.knnreg Predictions from k-Nearest Neighbors Regression Model
predict.gafs Predict new samples
plsda Partial Least Squares and Sparse Partial Least Squares Discriminant Analysis
resamples Collation and Visualization of Resampling Results
resampleSummary Summary of resampled performance estimates
prcomp.resamples Principal Components Analysis of Resampling Results
as.matrix.confusionMatrix Confusion matrix as a table
resampleHist Plot the resampling distribution of the model statistics
avNNet Neural Networks Using Model Averaging
print.train Print Method for the train Class
var_seq Sequences of Variables for Tuning
varImp.gafs Variable importances for GAs and SAs
gafsControl Control parameters for GA and SA feature selection
sbf Selection By Filtering (SBF)
update.safs Update or Re-fit a SA or GA Model
update.train Update or Re-fit a Model
trainControl Control parameters for train
rfeControl Controlling the Feature Selection Algorithms
SLC14_1 Simulation Functions
sbfControl Control Object for Selection By Filtering (SBF)
rfe Backwards Feature Selection
varImp Calculation of variable importance for regression and classification models
spatialSign Compute the multivariate spatial sign
xyplot.resamples Lattice Functions for Visualizing Resampling Results
safs_initial Ancillary simulated annealing functions
safs Simulated annealing feature selection
summary.bagEarth Summarize a bagged earth or FDA fit
train Fit Predictive Models over Different Tuning Parameters
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