Unboxing Tree Ensembles for interpretability: a hierarchical visualization tool and a multivariate optimal re-built tree

TL;DR

This paper introduces a hierarchical visualization tool and a MILP-based method to create an interpretable, sparse, multivariate tree that approximates complex ensemble models, enhancing understanding of their decision processes.

Contribution

It develops a novel hierarchical visualization and an MILP formulation for building an interpretable surrogate tree that mimics ensemble models with feature importance considerations.

Findings

The surrogate tree effectively approximates ensemble predictions.

The visualization highlights feature importance and usage levels.

The MILP approach produces shallow, interpretable trees with high fidelity.

Abstract

The interpretability of models has become a crucial issue in Machine Learning because of algorithmic decisions' growing impact on real-world applications. Tree ensemble methods, such as Random Forests or XgBoost, are powerful learning tools for classification tasks. However, while combining multiple trees may provide higher prediction quality than a single one, it sacrifices the interpretability property resulting in "black-box" models. In light of this, we aim to develop an interpretable representation of a tree-ensemble model that can provide valuable insights into its behavior. First, given a target tree-ensemble model, we develop a hierarchical visualization tool based on a heatmap representation of the forest's feature use, considering the frequency of a feature and the level at which it is selected as an indicator of importance. Next, we propose a mixed-integer linear programming (MILP) formulation for constructing a single optimal multivariate tree that accurately mimics the target model predictions. The goal is to provide an interpretable surrogate model based on oblique hyperplane splits, which uses only the most relevant features according to the defined forest's importance indicators. The MILP model includes a penalty on feature selection based on their frequency in the forest to further induce sparsity of the splits. The natural formulation has been strengthened to improve the computational performance of {mixed-integer} software. Computational experience is carried out on benchmark datasets from the UCI repository using a state-of-the-art off-the-shelf solver. Results show that the proposed model is effective in yielding a shallow interpretable tree approximating the tree-ensemble decision function.