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Found 6 papers, 3 papers with code
Rule Mining for Correcting Classification Models
In this study, we consider scenarios where developers should be careful to change the prediction results by the model correction, such as when the model is part of a complex system or software.
Counterfactual Explanation with Missing Values
Then, we propose a new framework of CE, named Counterfactual Explanation by Pairs of Imputation and Action (CEPIA), that enables users to obtain valid actions even with missing values and clarifies how actions are affected by imputation of the missing values.
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization
Given thousands of equally accurate machine learning (ML) models, how can users choose among them?
Exploring the Whole Rashomon Set of Sparse Decision Trees
We show three applications of the Rashomon set: 1) it can be used to study variable importance for the set of almost-optimal trees (as opposed to a single tree), 2) the Rashomon set for accuracy enables enumeration of the Rashomon sets for balanced accuracy and F1-score, and 3) the Rashomon set for a full dataset can be used to produce Rashomon sets constructed with only subsets of the data set.
Ordered Counterfactual Explanation by Mixed-Integer Linear Optimization
One of the popular methods is Counterfactual Explanation (CE), also known as Actionable Recourse, which provides a user with a perturbation vector of features that alters the prediction result.
Multi Instance Learning For Unbalanced Data
In the context of Multi Instance Learning, we analyze the Single Instance (SI) learning objective.
