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Found 5 papers, 2 papers with code
Comparing Machine Learning Algorithms by Union-Free Generic Depth
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions.
Evaluating machine learning models in non-standard settings: An overview and new findings
Our findings corroborate the concern that standard resampling methods often yield biased GE estimates in non-standard settings, underscoring the importance of tailored GE estimation.
Depth Functions for Partial Orders with a Descriptive Analysis of Machine Learning Algorithms
We propose a framework for descriptively analyzing sets of partial orders based on the concept of depth functions.
Statistical Comparisons of Classifiers by Generalized Stochastic Dominance
This yields indeed a powerful framework for the statistical comparison of classifiers over multiple data sets with respect to multiple quality criteria simultaneously.
Tree Ensembles with Rule Structured Horseshoe Regularization
The aggressive noise shrinkage of our prior also makes it possible to complement the rules from boosting in Friedman and Popescu (2008) with an additional set of trees from random forest, which brings a desirable diversity to the ensemble.
