{"type":"ScholarlyArticle","title":"A Fast and Unified Partial Dependence Plot Algorithm","authors":[{"name":"Ron Wettenstein","slug":"ron-wettenstein","given":"Ron","family":"Wettenstein","affiliation":""},{"name":"Alexander Nadel","slug":"alexander-nadel","given":"Alexander","family":"Nadel","affiliation":""},{"name":"Udi Boker","slug":"udi-boker","given":"Udi","family":"Boker","affiliation":""}],"abstract":"Partial Dependence Plots (PDPs) visualize how changes in a single feature affect the average model prediction. They are widely used in practice to interpret decision tree ensembles and other machine learning models. Joint-PDPs extend this idea to pairs of features, revealing their combined effect. Partial Dependence Interaction Values (PDIVs) measure feature interactions. The Any-Order-PDIVs task computes these interactions for every feature subset across all rows of the dataset.\r\n\r\nWe introduce WOODELF++, a unified and efficient approach for computing all these useful explainability tools on decision tree ensembles, building on WOODELF, an algorithm for efficient SHAP computation. By deriving suitable metrics over pseudo-Boolean functions, WOODELF++ can compute PDPs (exact and approximate), Joint-PDPs, and Any-Order-PDIVs in a unified framework. Our method delivers substantial complexity improvements over the state of the art, including an exponential gain for Any-Order-PDIVs. Additionally, we introduce and efficiently compute Full PDPs, which leverage the model’s split thresholds to faithfully capture its behavior across all possible feature values.\r\n\r\nWOODELF++ is implemented in pure Python and supports GPU acceleration. On a dataset with 400,000 rows, WOODELF++ computes PDP and Joint-PDP up to 6x faster than the state of the art and up to five orders of magnitude faster than scikit-learn. For Any-Order-PDIVs, the gap is even larger: WOODELF++ computes all interaction values in 5 minutes, while the state of the art is estimated to require over 1,000,000 years.","doi":"10.24963/ijcai.2026/562","url":"https://ijcai.org/proceedings/2026/562","pdf":"https://ijcai.org/proceedings/2026/0562.pdf","markdown":"https://ijcai.org/proceedings/2026/562.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/562","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Machine Learning","track":"Main Track","number":562,"pages":{"first":5048,"last":5056},"keywords":["Machine Learning: Explainable/Interpretable machine learning","Machine Learning: Game Theory","Machine Learning: Ensemble methods"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}