`partial_dependence` – Friedman (2001) partial dependence plot.#

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Operational op under axis op, sub-layer L7_A_importance_dag_body, layer l7. Standalone callable: mf.functions.partial_dependence_importance.

Function signature#

mf.functions.partial_dependence_importance(
    result: FitResultBase,
    X: np.ndarray | pd.DataFrame,
) -> PDPImportanceResult

Parameters#

name	type	default	constraint	description
`result`	`FitResultBase`	—	—	Fitted result object exposing ._model (the raw sklearn estimator). Returned by any L4 standalone callable such as mf.functions.ridge_fit, mf.functions.random_forest_fit, etc.
`X`	`np.ndarray	pd.DataFrame`	—	—

Returns#

PDPImportanceResult — frozen dataclass with fit results.

Attribute	Type	Description
`.importances_`	`np.ndarray`	PDP range (max - min) per feature, shape (n_features,).
`.feature_names_`	`list[str]`	Feature names.
`.pdp_values_`	`dict[str, np.ndarray]`	Mean predictions at grid points per feature.
`.grid_values_`	`dict[str, np.ndarray]`	Grid evaluation points per feature.
`.summary(top_n=10)`	`str`	Human-readable text table.

Behavior#

For feature j, computes E_{X_{-j}}[f(x_j, X_{-j})] over a grid of x_j values. Visualises the marginal effect of x_j on the prediction averaged over the joint distribution of remaining features. sklearn partial_dependence backend.

When to use

Visualising marginal feature effects; first-pass non-linearity audit.

When NOT to use

Highly correlated features – PDP averages over impossible regions of feature space. Use accumulated_local_effect instead.

In recipe context#

Set params.op = "partial_dependence" in the relevant layer to activate this op within a recipe:

# Layer L7 recipe fragment
params:
  op: partial_dependence

References#

macroforecast design Part 3, L7: ‘every importance op produces (table, figure) pairs; the L7.B sub-layer governs export shape.’
Friedman (2001) ‘Greedy Function Approximation: A Gradient Boosting Machine’, Annals of Statistics 29(5): 1189-1232.

partial_dependence – Friedman (2001) partial dependence plot.#