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Activity Number: 228 - Interpreting Machine Learning Models: Opportunities, Challenges, and Applications
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #301752 Presentation
Title: Understanding the Effects of Predictor Variables in Black-Box Supervised Learning Models
Author(s): Daniel W Apley*
Companies: Northwestern University
Keywords: visualization; deep learning; explainable machine learning; support vector machine; partial dependence plot; variable importance

A shortcoming of most black-box supervised learning models is their lack of interpretability or transparency. Partial dependence (PD) plots are the most popular general approach for helping to visualize the effects of the predictor variables, but they can produce incorrect results with strongly correlated predictors, because they require extrapolation beyond the training data envelope. Functional ANOVA for correlated inputs can avoid this extrapolation but involves prohibitive computational expense and subjective choice of additive surrogate model to fit to the supervised learning model. We present a new visualization approach that we term accumulated local effects (ALE) plots, which have a number of advantages over existing methods. First, ALE plots do not require unreliable extrapolation with correlated predictors. Second, they are orders of magnitude less computationally expensive than PD plots, and many orders of magnitude less expensive than functional ANOVA. Third, they yield convenient variable importance/sensitivity measures that possess a number of desirable properties for quantifying the impact of each predictor.

Authors who are presenting talks have a * after their name.

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