Fast Estimation of Partial Dependence Functions using Trees

Jinyang Liu, Tessa Steensgaard, Marvin N. Wright, Niklas Pfister, Munir Hiabu

TL;DR: We introduce an efficient algorithm for computing partial dependence functions for tree-based model explanations

Abstract: Many existing interpretation methods are based on Partial Dependence (PD) functions that, for a pre-trained machine learning model, capture how a subset of the features affects the predictions by averaging over the remaining features. Notable methods include Shapley additive explanations (SHAP) which computes feature contributions based on a game theoretical interpretation and PD plots (i.e., 1-dim PD functions) that capture average marginal main effects. Recent work has connected these approaches using a functional decomposition and argues that SHAP values can be misleading since they merge main and interaction effects into a single local effect. However, a major advantage of SHAP compared to other PD-based interpretations has been the availability of fast estimation techniques, such as `TreeSHAP`. In this paper, we propose a new tree-based estimator, `FastPD`, which efficiently estimates arbitrary PD functions. We show that `FastPD` consistently estimates the desired population quantity -- in contrast to path-dependent `TreeSHAP` which is inconsistent when features are correlated. For moderately deep trees, `FastPD` improves the complexity of existing methods from quadratic to linear in the number of observations. By estimating PD functions for arbitrary feature subsets, `FastPD` can be used to extract PD-based interpretations such as SHAP, PD plots and higher-order interaction effects.

Lay Summary: Many powerful AI systems work by combining hundreds of simple “if-then” rules to make predictions—but these systems are often opaque, making it difficult to understand why they reach a particular decision. To address this, we introduce FastPD, an efficient algorithm that breaks down a model’s output into understandable parts, showing how each input factor and their combinations contribute to a prediction. FastPD runs in time that grows linearly with the amount of data, instead of quadratically, by reusing calculations across data points, and its explanations become exact as more background data are used. By cutting explanation times and clearly revealing how each factor and its interactions drive a prediction, FastPD empowers analysts, regulators, and everyday users to trust, debug, and manage AI-driven decisions more effectively.

Link To Code: https://github.com/PlantedML/glex

Primary Area: Social Aspects->Accountability, Transparency, and Interpretability

Keywords: partial dependence, model explanation, functional decomposition

Submission Number: 10548