Partial Dependence Plots
Partial Dependence Plots (PDPs) are a model-agnostic visualization technique used in machine learning to understand the relationship between a feature and the predicted outcome of a model, while averaging out the effects of all other features. They show how the average prediction changes as a specific feature varies across its range, holding other features constant at their observed values. PDPs help interpret complex models like random forests or gradient boosting by revealing marginal effects and potential interactions.
Developers should use PDPs when building or deploying machine learning models to ensure interpretability and trust, especially in regulated industries like finance or healthcare where model decisions must be explained. They are particularly useful for identifying non-linear relationships, detecting feature interactions, and validating model behavior against domain knowledge, such as checking if a feature's impact aligns with expectations in a predictive model.