LionForests: Local Interpretation of Random Forests

TL;DR

LionForests introduces a novel method for interpreting random forests by using unsupervised learning and similarity metrics, transforming black-box models into understandable rules to enhance trust and transparency.

Contribution

The paper presents LionForests, a new approach that makes random forests interpretable through unsupervised techniques and similarity measures, revealing transparent rules from ensemble predictions.

Findings

Enables interpretation of random forests as simple rules

Uses unsupervised learning to explore forest structure

Improves trust in machine learning predictions

Abstract

Towards a future where machine learning systems will integrate into every aspect of people's lives, researching methods to interpret such systems is necessary, instead of focusing exclusively on enhancing their performance. Enriching the trust between these systems and people will accelerate this integration process. Many medical and retail banking/finance applications use state-of-the-art machine learning techniques to predict certain aspects of new instances. Tree ensembles, like random forests, are widely acceptable solutions on these tasks, while at the same time they are avoided due to their black-box uninterpretable nature, creating an unreasonable paradox. In this paper, we provide a methodology for shedding light on the predictions of the misjudged family of tree ensemble algorithms. Using classic unsupervised learning techniques and an enhanced similarity metric, to wander among transparent trees inside a forest following breadcrumbs, the interpretable essence of tree ensembles arises. An interpretation provided by these systems using our approach, which we call "LionForests", can be a simple, comprehensive rule.