Model Agnostic Supervised Local Explanations (1807.02910v3)

Abstract: Model interpretability is an increasingly important component of practical machine learning. Some of the most common forms of interpretability systems are example-based, local, and global explanations. One of the main challenges in interpretability is designing explanation systems that can capture aspects of each of these explanation types, in order to develop a more thorough understanding of the model. We address this challenge in a novel model called MAPLE that uses local linear modeling techniques along with a dual interpretation of random forests (both as a supervised neighborhood approach and as a feature selection method). MAPLE has two fundamental advantages over existing interpretability systems. First, while it is effective as a black-box explanation system, MAPLE itself is a highly accurate predictive model that provides faithful self explanations, and thus sidesteps the typical accuracy-interpretability trade-off. Specifically, we demonstrate, on several UCI datasets, that MAPLE is at least as accurate as random forests and that it produces more faithful local explanations than LIME, a popular interpretability system. Second, MAPLE provides both example-based and local explanations and can detect global patterns, which allows it to diagnose limitations in its local explanations.

Analyzing MAPLE: A Model Agnostic Supervised Local Explanation System

The paper, “Model Agnostic Supervised Local Explanations,” introduces MAPLE, a novel methodology developed to tackle a persistent challenge within the field of machine learning—balancing model interpretability with prediction accuracy. Plumb, Molitor, and Talwalkar propose this system with dual objectives: to provide accurate predictions akin to black-box models and to offer interpretable insights into model behavior, vital for their application in critical decision-making domains.

Technical Insights of MAPLE

MAPLE is distinguished by its fusion of local linear models and random forests into a cohesive explanation framework. This hybrid design affords MAPLE two main strengths over contemporary interpretability systems. Firstly, it delivers competitive predictive accuracy while ensuring the self-explanations it generates are highly interpretable. This effectively circumvents the conventional trade-off between accuracy and interpretability observed in models like LIME, which prioritize explanation over predictive precision. Secondly, the system employs a unified approach allowing it to offer both local and example-based explanations while also being capable of uncovering global patterns.

Empirical Results and Comparisons

The efficacy of MAPLE is substantiated on multiple UCI datasets where it demonstrates predictive accuracy on par with, or superior to, random forests and gradient boosted regression trees. A notable empirical finding is that MAPLE surpasses LIME in generating faithful local explanations of black-box models. Specifically, the system offers more precise approximations of black-box responses, a characteristic imperative for its role as a reliable explanatory tool in high-stakes domains.

Implications and Future Directions

The introduction of MAPLE bears substantial implications for interpretability research and application. By synchronizing feature selection with model explanation efforts, it enhances our capacity to draw insightful conclusions from complex datasets. Furthermore, the method’s adeptness at identifying global patterns from local training distributions addresses a critical shortcoming of most local explanation systems—their occasionally limited scope and sensitivity to abrupt data shifts.

Looking forward, the MAPLE framework opens up avenues for further research. It encourages exploration into integrating local feature selection mechanisms, potentially harnessing the decision path information through tree ensembles. Additionally, its foundation on influence functions hints at innovative applications, such as employing Cook's distance for determining data point leverage, thereby boosting model robustness and detection of anomalous inputs.

In conclusion, the MAPLE methodology presents a pragmatic stride towards harmonizing the dual objectives of high interpretability and accuracy, signifying its potential to significantly advance the development and deployment of interpretable machine learning models. The research not only contributes a novel perspective but also paves the way for future explorations in fostering trust and insight into machine learning systems.