`permutation_importance_strobl` – Strobl (2008) conditional permutation importance.#

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

Function signature#

mf.functions.cond_permutation_importance(
    result: FitResultBase,
    X: np.ndarray | pd.DataFrame,
    y: np.ndarray | pd.Series,
) -> CondPermutationImportanceResult

Parameters#

name	type	default	constraint	description
`result`	`FitResultBase`	—	—	Fitted result object exposing ._model (the raw sklearn estimator). Returned by any L4 standalone callable.
`X`	`np.ndarray	pd.DataFrame`	—	—
`y`	`np.ndarray	pd.Series`	—	—

Returns#

CondPermutationImportanceResult — frozen dataclass with fit results.

Attribute	Type	Description
`.importances_mean_`	`np.ndarray`	Mean conditional permutation importance, shape (n_features,).
`.importances_std_`	`np.ndarray`	Std deviation over n_repeats, shape (n_features,).
`.feature_names_`	`list[str]`	Feature names.
`.method`	`str`	‘strobl’ – Strobl (2008) conditional permutation.
`.summary(top_n=10)`	`str`	Human-readable text table.

Behavior#

Permutes x_j only within bins defined by the joint distribution of correlated predictors, eliminating the extrapolation bias of plain permutation importance for correlated features. v0.3 implementation uses tree-partition bins (Strobl et al. 2008 §4).

When to use

Highly correlated macro panels (FRED-MD / -QD with redundant aggregates).

When NOT to use

When predictor correlations are negligible – the cheaper plain permutation importance suffices.

In recipe context#

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

# Layer L7 recipe fragment
params:
  op: permutation_importance_strobl

References#

macroforecast design Part 3, L7: ‘every importance op produces (table, figure) pairs; the L7.B sub-layer governs export shape.’
Strobl, Boulesteix, Kneib, Augustin & Zeileis (2008) ‘Conditional variable importance for random forests’, BMC Bioinformatics 9: 307.

permutation_importance_strobl – Strobl (2008) conditional permutation importance.#