revised MLearn interface for machine learning
generate a partition function for cross-validation, where the partitions are approximately balanced with respect to the distribution of a response variable
container for clustering outputs in uniform structure
Compute the confusion matrix for a classifier.
Compute confusion tables for a confusion matrix.
support for feature selection in cross-validation
extract history of feature selection for a cross-validated machine learner
shiny-oriented GUI for cluster or classifier exploration
Class "learnerSchema" - convey information on a machine learning function to the MLearn wrapper
Methods for Function planarPlot in Package 'MLInterfaces'
shiny app for interactive 3D visualization of mlbench hypercube
Predict method for 'classifierOutput' objects
create learned tesselation of feature space after PC transformation
Class "varImpStruct" - collect data on variable importance from various machine learning methods
Cross-validation in clustered computing environments
container for information specifying a cross-validated machine learning exercise