Kısmi Bağımlılık Sezgisi

Importance tells you a feature matters, but not how. A partial dependence plot shows the direction and shape of that effect. 📈

It answers a clean question: if I change just this one feature, how does the average prediction move while everything else stays put?

The method sweeps a feature across its range, predicts at each value for every row, then averages those predictions into one curve.

A rising line means more of the feature lifts predictions. A flat line means the model barely reacts to that feature at all.

Partial dependence shines on non-linear models: the curve can climb, plateau, or dip, revealing effects a single coefficient would hide.

scikit-learn draws these for you with PartialDependenceDisplay, given your model, data, and the feature to inspect.

Pass two features and you get a 2D heatmap showing how their interaction bends the prediction surface together.

An ICE plot draws one line per row instead of an average, so you can spot subgroups that respond differently.

Partial dependence assumes features are roughly independent. Strong correlations can create unrealistic combinations and mislead the average curve.

SHAP attributes credit per prediction, while partial dependence describes the overall shape of one feature effect across the dataset.

A simple up-or-down curve is easy for non-experts to grasp, turning a black-box model into a clear story about cause and effect.

What does a partial dependence plot actually show?

You saw how partial dependence turns a feature into a readable curve, with ICE for detail and SHAP for per-row credit. Next, charts that persuade. 🎯