`shap_linear` – Linear SHAP – closed-form Shapley values for linear models.#

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

Function signature#

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

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#

SHAPImportanceResult — frozen dataclass with fit results.

Attribute	Type	Description
`.shap_values_`	`np.ndarray`	SHAP values, shape (n_samples, n_features).
`.expected_value_`	`float`	SHAP base value (expected model output).
`.feature_names_`	`list[str]`	Feature names.
`.explainer_type`	`str`	Explainer used (LinearExplainer / KernelExplainer).
`.summary(top_n=10)`	`str`	Table of mean absolute SHAP values.

Behavior#

For a fitted linear model f(x) = β'x + b, the SHAP value for feature j reduces to β_j (x_j - E[x_j]). Uses the training-sample mean as the reference. Available for every linear L4 family.

When to use

Linear models when the SHAP per-row decomposition is needed (otherwise model_native_linear_coef suffices).

In recipe context#

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

# Layer L7 recipe fragment
params:
  op: shap_linear

References#

macroforecast design Part 3, L7: ‘every importance op produces (table, figure) pairs; the L7.B sub-layer governs export shape.’
Lundberg & Lee (2017) ‘A Unified Approach to Interpreting Model Predictions’, NeurIPS 30: 4765-4774.

shap_linear – Linear SHAP – closed-form Shapley values for linear models.#