Consistent Sufficient Explanations and Minimal Local Rules for explaining regression and classification models (2111.04658v2)

Abstract: To explain the decision of any model, we extend the notion of probabilistic Sufficient Explanations (P-SE). For each instance, this approach selects the minimal subset of features that is sufficient to yield the same prediction with high probability, while removing other features. The crux of P-SE is to compute the conditional probability of maintaining the same prediction. Therefore, we introduce an accurate and fast estimator of this probability via random Forests for any data $(\boldsymbol{X}, Y)$ and show its efficiency through a theoretical analysis of its consistency. As a consequence, we extend the P-SE to regression problems. In addition, we deal with non-discrete features, without learning the distribution of $\boldsymbol{X}$ nor having the model for making predictions. Finally, we introduce local rule-based explanations for regression/classification based on the P-SE and compare our approaches w.r.t other explainable AI methods. These methods are available as a Python package at \url{www.github.com/salimamoukou/acv00}.

• The paper extends the Probabilistic Sufficient Explanations (P-SE) framework to regression by generalizing the Same Decision Probability (SDP), ensuring minimal yet consistent explanations.
• It introduces an efficient SDP estimator using Random Forests, which bypasses explicit feature distribution assumptions and enhances computational practicality.
• The study proposes minimal local rule-based explanations that robustly quantify feature importance, bolstering transparency in high-stakes machine learning applications.

Overview of "Consistent Sufficient Explanations and Minimal Local Rules for Explaining any Classifier or Regressor"

This paper introduces a novel approach to explain machine learning models through the extension and generalization of Probabilistic Sufficient Explanations (P-SE). The authors present a consistent methodology that enables the extraction of minimal subsets of features, which can maintain the predictive output of a model with high probability. This approach addresses both classification and regression models and incorporates mechanisms to handle continuous and non-discrete features without requiring the estimation of the feature distribution or direct access to the model.

Key Contributions

1. Extension of P-SE to Regression: The work extends the concept of Same Decision Probability (SDP) from classification to regression tasks. The SDP represents the probability of maintaining the same model prediction when reducing the feature space. This allows practitioners to derive Sufficient Explanations for regression, providing a probabilistic framework that captures model behavior more accurately across different types of tasks.
2. Efficient Estimation via Random Forests: The authors propose a computationally efficient estimator for the SDP using Random Forests. This is significant because it enables the approximation of SDP without explicit distributional assumptions for the features, leveraging the Random Forests' ability to approximate conditional distributions consistently.
3. Probabilistic Local Explanatory Importance: The researchers introduce a method to quantify the importance of features locally, which provides insight into the diversity of Sufficient Explanations. This metric evaluates how often each feature appears in the set of all Sufficient Explanations, offering a robust alternative to traditional feature importance measures in explainable AI (XAI).
4. Local Rule-Based Explanations: The paper proposes local rule-based explanations that extend Sufficient Explanations into interpretable rules. These Minimal Sufficient Rules capture interactions within the feature set while ensuring minimality and consistency in the outcome. They not only enhance interpretability but also provide direct insights into decision boundaries with high precision.

Implications and Future Directions

The methodologies introduced have substantial implications for improving the reliability and interpretability of machine learning models in high-stakes environments where understanding model behavior is critical. By enabling consistent and probabilistically sound explanations, this work advances current XAI efforts by addressing limitations found in methods like SHAP and LIME, especially under dependencies among features.

The proposed framework can be particularly beneficial in domains such as healthcare and finance, where model transparency is as crucial as predictive accuracy. As future developments, the extension of these ideas to include uncertainty quantification and broader model classes (e.g., deep learning architectures) could further push the boundaries of XAI, bridging the gap between model performance and interpretability.

The availability of a Python package for implementing these methods emphasizes the practical applicability of the research and supports broader adoption in the community. Researchers and practitioners can leverage this tool to derive more nuanced insights into models' decision-making processes, bolstering the trust and understanding of complex AI systems.