LionForests: Local Interpretation of Random Forests
arXiv:1911.08780
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.
arXiv comment8 Pages, 4 Tables, 6 Figures, Submitted to NeHuAI-2020 Workshop of ECAI2020
References in corpus (6)
- Scikit-learn: Machine Learning in Python
- All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
- Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
- Explainable AI for Trees: From Local Explanations to Global Understanding
- Rule Extraction Algorithm for Deep Neural Networks: A Review
- Interpreting Models via Single Tree Approximation