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Found 5 papers, 2 papers with code
Generating collective counterfactual explanations in score-based classification via mathematical optimization
By means of novel Mathematical Optimization models, we provide a counterfactual explanation for each instance in a group of interest, so that the total cost of the perturbations is minimized under some linking constraints.
Supervised Feature Compression based on Counterfactual Analysis
Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning.
On Clustering Categories of Categorical Predictors in Generalized Linear Models
We propose a method to reduce the complexity of Generalized Linear Models in the presence of categorical predictors.
Optimal randomized classification trees
Our classifier can be seen as a randomized tree, since at each node of the decision tree a random decision is made.
Sparsity in Optimal Randomized Classification Trees
In this paper, we propose a continuous optimization approach to build sparse optimal classification trees, based on oblique cuts, with the aim of using fewer predictor variables in the cuts as well as along the whole tree.
