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Also known as: PDP
An explainability method showing a model’s average prediction as selected features vary, marginalizing over other features.
DESCRIPTION
Partial dependence plots visualize the marginal effect of one or two features on a model’s predictions, averaging over the distribution of other features. They help assess monotonicity, saturation, and interaction effects. PDP assumes feature independence; correlated features can yield misleading interpretations, motivating ALE or ICE as alternatives. PDP is supported in scikit-learn and many platforms. It is typically used for global interpretability of black-box models.
ALTERNATIVE DEFINITIONS
scikit-learn
Partial dependence plots show the relationship between the target and a set of ‘features’, marginalizing over the values of all other features.
Stresses marginalization and global effect over the dataset.
Interpretable Machine Learning — Christoph Molnar
Partial dependence plots (PDP) show the marginal effect one or two features have on the predicted outcome of a machine learning model.
Highlights one- and two-feature visualization and marginal effect.
Google Cloud
A partial dependence plot shows how a feature affects predictions, averaged over the dataset.
Emphasizes averaging across the dataset in production tools.
RELATED TERMS
SHAP Values LIME Feature Importance Sensitivity Analysis Global Surrogate Model Model Explainability Partial Regression Plot Causal Inference
COMPETING TERMS
Accumulated Local Effects Individual Conditional Expectation
RELATIONS
- Broader: Dependence
KNOWLEDGE AREAS
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer Data Analyst BI Specialist
EXTERNAL SOURCES
- Wikipedia - Partial dependence plot
- scikit-learn - API and examples for PDP
- Interpretable ML - Conceptual guide with examples
- Google Cloud - Explainable AI tooling overview
TOP QUESTIONS
What is Partial Dependence in machine learning?
It visualizes a model’s average predicted response as selected features vary, marginalizing over other features to show global effects.
Category: Definition
When should Partial Dependence be used?
Use it to understand global trends and feature effects in complex models, especially for business interpretability and model validation.
Category: Application
What is a hot topic around Partial Dependence?
Debates on correlated features causing misleading PDPs and preference for ALE or ICE in such cases are prominent.
Category: Topic
What’s a key limitation of Partial Dependence?
It assumes feature independence; strong correlations can distort the averaged effect.
Category: Risks
How do PDP and ICE differ?
PDP averages across instances; ICE shows individual curves, revealing heterogeneity and interactions.
Category: Comparison
How to compute Partial Dependence efficiently?
Sample instances, vary the feature grid, and average predictions; leverage model APIs or scikit-learn utilities.
Category: HowTo
What are common mistakes with Partial Dependence?
Ignoring feature correlation, using too coarse grids, and misreading extrapolated regions beyond data support.
Category: Mistakes
How is Partial Dependence used in regulated AI?
It supports transparency by explaining feature influence, complementing documentation like model cards and risk assessments.
Category: Governance
Where to learn Partial Dependence?
Read scikit-learn docs, Molnar’s Interpretable ML book, and vendor explainable AI guides.
Category: Learning