`model_native_tree_importance` – Mean-decrease-impurity importance from a fitted tree ensemble.#

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

Function signature#

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

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#

NativeImportanceResult — frozen dataclass with fit results.

Attribute	Type	Description
`.importances_`	`np.ndarray`	MDI feature_importances_ values, shape (n_features,).
`.feature_names_`	`list[str]`	Feature names matching importances_.
`.method`	`str`	‘tree_native’ – method descriptor.
`.summary(top_n=10)`	`str`	Human-readable text table sorted by descending importance.

Behavior#

Returns sklearn’s feature_importances_ for the fitted estimator – the average reduction in node impurity attributable to each feature, weighted by node sample count. Available for every tree-family L4 model (decision_tree / random_forest / extra_trees / gradient_boosting / xgboost / lightgbm / catboost).

Cheap and built-in; biases toward high-cardinality features. For unbiased tree importance, prefer permutation_importance or permutation_importance_strobl.

When to use

Quick first-pass tree importance; pair with permutation importance for bias-correction.

When NOT to use

High-cardinality continuous features dominate – known MDI bias (Strobl et al. 2007).

In recipe context#

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

# Layer L7 recipe fragment
params:
  op: model_native_tree_importance

References#

macroforecast design Part 3, L7: ‘every importance op produces (table, figure) pairs; the L7.B sub-layer governs export shape.’
Breiman (2001) ‘Random Forests’, Machine Learning 45(1): 5-32.
Strobl, Boulesteix, Zeileis & Hothorn (2007) ‘Bias in random forest variable importance measures’, BMC Bioinformatics 8: 25.

model_native_tree_importance – Mean-decrease-impurity importance from a fitted tree ensemble.#