Explainability✓ Mathematical
◆ The PatternHow does changing one feature affect predictions? PDP shows the average, ICE shows every instance
Partial Dependence Plots (PDP) show the average effect of one feature on predictions, marginalising over all other features. ICE plots show the same for each individual instance — revealing heterogeneity the average hides.
PD(xs) = (1/n) · Σ f(xs, xc(i))
PD = average prediction when feature s is fixed at xs, averaging over all other features.
// Interactive — PDP line with individual ICE curves
Show ICE
Instances20
# Python — PDP & ICE from sklearn.inspection import PartialDependenceDisplay # PDP with ICE PartialDependenceDisplay.from_estimator( model, X_train, features=['age', 'income'], kind='both', # PDP + ICE ice_lines_kw={'alpha': 0.1} )
Interactions: If ICE lines cross each other, there's a feature interaction — the effect of this feature depends on other features' values. A flat PDP with scattered ICE means the average is misleading.