DataTerminology.comNEW
Also known as: PDP
A visualization showing the marginal effect of one or two features on a model’s predictions.
DESCRIPTION
A Partial Dependence Plot (PDP) illustrates how a model’s prediction changes with variations in one or two features, averaging out the influence of all other features. It provides a global view of feature effects and can reveal non-linearities or thresholds learned by the model. PDPs are model-agnostic and apply to tree ensembles, linear models, and neural networks. They help diagnose spurious correlations and inform feature engineering. However, PDPs assume feature independence, which can mislead when features are strongly correlated. For correlated features, Individual Conditional Expectation (ICE) curves and Accumulated Local Effects (ALE) plots are often preferred. PDPs complement local explainers like SHAP and LIME by providing a broader perspective on behavior.
ALTERNATIVE DEFINITIONS
scikit-learn
Partial dependence plots (PDP) show the marginal effect one or two features have on the predicted outcome of a machine learning model.
Emphasizes one or two features and marginal effect on predictions.
Interpretable Machine Learning (Molnar)
Partial dependence plots show the marginal effect of one or two features on the predicted outcome of a machine learning model.
Highlights model-agnostic nature and provides practical guidance and caveats.
Wikipedia
A partial dependence plot (PDP) depicts the marginal effect of a selected set of features on the predicted outcome of a machine learning model.
Concise encyclopedic definition focusing on marginal effects.
RELATED TERMS
Individual Conditional Expectation LIME Permutation Importance Global Surrogate Model Model Explainability Feature Importance Explainable AI Sensitivity Analysis
COMPETING TERMS
Accumulated Local Effects SHAP
KNOWLEDGE AREAS
AI Data Science MLOps Testing & Validation
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer Data Analyst Data Management Consultant
EXTERNAL SOURCES
- Wikipedia - Partial dependence plot
- scikit-learn - Official PDP documentation and examples
- Interpretable Machine Learning (Molnar) - Practical guidance and caveats
- arXiv - Research on ALE vs. PDP
TOP QUESTIONS
What is a Partial Dependence Plot and when is it useful?
A Partial Dependence Plot shows how a model’s prediction changes with a feature, averaging out others. It’s useful for global interpretability and detecting non-linear relationships.
Category: Definition
How does Partial Dependence Plot differ from Individual Conditional Expectation?
PDP averages effects across all instances, giving a global view, while ICE displays instance-level curves, revealing heterogeneity and interactions hidden by averaging.
Category: Comparison
What assumptions underpin a Partial Dependence Plot?
PDP assumes the target feature is independent from other features. When features are correlated, PDP can be misleading due to extrapolation into unrealistic combinations.
Category: Assumptions
When should Accumulated Local Effects be preferred over Partial Dependence Plots?
Use ALE when features are correlated, as ALE limits extrapolation by integrating local effects, yielding more reliable estimates than PDP under correlation.
Category: BestPractice
Can Partial Dependence Plots handle interactions between two features?
Yes, 2D PDPs visualize joint effects of two features, revealing interactions and decision boundaries learned by the model.
Category: Capabilities
What are common mistakes when interpreting Partial Dependence Plots?
Common mistakes include ignoring feature correlation, over-interpreting flat lines with sparse data, and assuming causality from observed associations.
Category: Mistakes
How do Partial Dependence Plots relate to SHapley Additive exPlanations?
PDPs give global average effects, while SHAP provides local additive attributions that aggregate to global importance; they are complementary perspectives.
Category: Relation
How to compute a Partial Dependence Plot efficiently for tree ensembles?
Use model-specific algorithms like scikit-learn’s fast tree-based PDP computation, which avoids brute-force re-prediction for each grid point.
Category: HowTo
What is a limitation of Partial Dependence Plots in high dimensions?
Beyond two features, PDPs become hard to visualize and interpret, and averaging can mask complex interactions.
Category: Limitations