Alike Parts: A Feature-Informed Approach to Local and Global Prototype Explanations

2026-05-23 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Expert, extended

Summary

Alike Parts is a new framework that enhances prototype-based explanations for machine learning black-box classifiers by integrating feature importance at two levels. For local explanations, it introduces "alike parts," a method that identifies and highlights the most relevant, shared feature subsets between a classified instance and its nearest prototype, guiding user attention. For global explanations, the framework augments the prototype selection objective function with a feature importance term, actively promoting diversity in the feature attributions of the chosen prototypes. Experiments on six benchmark datasets, including Apple Quality and Diabetes, demonstrated that this augmented selection process maintains or, in some cases, increases the prediction fidelity of the 1-NN surrogate model. The Python-based implementation, utilizing libraries like SHAP and TreeInterpreter, is publicly available on GitHub and supports various prototype generation algorithms like A-PETE and SM-A, particularly for Random Forest ensembles.

Key takeaway

For Machine Learning Engineers building interpretable black-box models, integrating feature importance into prototype-based explanations is crucial. You should use the "alike parts" method to highlight key shared features between instances and prototypes, improving local explanation clarity. Augmenting prototype selection with feature importance promotes diverse global prototypes, which can maintain or even increase surrogate model fidelity. Consider adjusting the $\beta$ parameter to fine-tune the balance between distance and feature importance in your prototype selection algorithms.

Key insights

Integrating feature importance into prototype selection enhances both local explanations and global prototype diversity without compromising model fidelity.

Principles

• Feature importance guides user attention in local prototype explanations.
• Diverse feature attributions in prototypes enhance global interpretability.
• Augmenting prototype selection with feature importance preserves fidelity.

Method

The method identifies "alike parts" by computing feature importance scores for an instance and its nearest prototype, then applying similarity operators (Hadamard, $l_1$, $l_2$) to create a weight vector. A binary mask is generated via mean thresholding or top-$k$ selection to highlight shared features.

In practice

• Use Tree SHAP or Tree Interpreter for feature importance.
• Apply mean thresholding for flexible feature subset selection.
• Adjust $\beta$ to balance distance and feature importance in prototype selection.

Topics

• Explainable AI
• Prototype-based Explanations
• Feature Importance
• Black-box Models
• Tabular Data
• Random Forest

Code references

• andosa/treeinterpreter
• jkarolczak/alike-parts

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.