A company makes forecasts each quarter to decide how to optimize operations to meet expected demand. The company uses ML models to make these…

Partial dependence plots (PDPs) are a primary tool for machine learning explainability. They illustrate the marginal effect of one or two features on the predicted outcome of a model, holding other features constant. For stakeholders, this provides a clear, visual representation of how key business drivers (e.g., price, marketing spend) influence the model's demand forecasts. This directly addresses the need for transparency into the model's logic and explainability of its predictions in an accessible format, fostering trust and better decision-making.

1. Molnar

C. (2022). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (2nd ed.). In Chapter 5.2

"Partial Dependence Plot (PDP)

" the author states

"The partial dependence plot (PDP or PD plot) shows the marginal effect one or two features have on the predicted outcome of a machine learning model... A partial dependence plot can show whether the relationship between the target and a feature is linear

monotonic or more complex." This directly supports using PDPs for explainability.

2. Friedman

J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. The Annals of Statistics

29(5)

1189–1232. In Section 8.2

"Interpretation

" Friedman introduces partial dependence plots as a method to understand the effect of specific variables on the model's predictions

stating they are "useful for gaining an understanding of the dependence of the function f(x) on the joint values of the corresponding subset of variables." (p. 1217). DOI: https://doi.org/10.1214/aos/1013203451

3. University of California

Berkeley. (n.d.). CS 189/289A: Introduction to Machine Learning

Lecture 18: Interpretability. Course materials discuss PDPs as a model-agnostic method for understanding feature effects

highlighting their role in explaining what a model has learned to a human.