Partial dependence plot
A partial dependence plot shows a model's average predicted response as selected features vary, averaging over the values of the remaining features.
Ensemble Methods for Machine Learning
A partial dependence function is the average model prediction obtained by fixing selected feature values and integrating or averaging over the remaining features.
- How it is used
- Explains global relationships between selected features and predictions for black-box models.
- How deep it goes
- Defines marginalization and gives a stepwise approximation, while stopping short of formal integral details.
- Examples
- Uses Bank Marketing variables, column replacement, averaged predictions, and four plotted PDPs.
- What you can do after
- Apply PDPs through scikit-learn’s PartialDependenceDisplay to visualize selected feature relationships.
Read how Ensemble Methods for Machine Learning explains partial dependence plot →
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
A partial dependence plot displays a model's average predicted response as one or more input features vary while marginalizing over other features.
- How it is used
- Uses model-agnostic charts to inspect feature–target relationships and guide numerical transformations.
- How deep it goes
- Defines averaging and independence assumptions, demonstrates plots, and stops short of resolving correlated-feature effects.
- Examples
- Uses Airbnb NYC numeric features, binary-feature boxplots, ICE panels, and conjoint plots for suspected interactions.
- What you can do after
- Apply PDPs through scikit-learn’s PartialDependenceDisplay to inspect a fitted XGBoost pipeline.
Related concepts
- marginalizationused by · Marginalization is the operation of summing or integrating over variables or alternatives that are not retained to obtain a distribution or quantity for the remaining variables.
- elasticity plotincludes · An elasticity plot shows how a model's predicted outcome changes as a price-related input is varied for a product or SKU.
- individual conditional expectation plotincludes · An individual conditional expectation plot shows how a model’s prediction changes for an individual observation as a feature varies.
- training datasetuses · A collection of examples or observations supplied to a learning procedure to fit or adapt a model or learn its patterns or parameters.
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