Module explain_generic 

Generic explainability for any FPC-based model.

Provides the FpcPredictor trait and generic functions that work with any model that implements it — including linear regression, logistic regression, and classification models (LDA, QDA, kNN).

The generic functions delegate to internal helpers from crate::explain.

Enums

TaskType

The type of prediction task a model solves.

Traits

FpcPredictor

Trait abstracting over any FPC-based model for generic explainability.

Functions

generic_ale

Generic ALE plot for an FPC component in any FPC-based model.

generic_anchor

Generic anchor explanation for any FPC-based model.

generic_conditional_permutation_importance

Generic conditional permutation importance for any FPC-based model.

generic_counterfactual

Generic counterfactual explanation for any FPC-based model.

generic_domain_selection

Generic domain selection using SHAP-based functional importance.

generic_friedman_h

Generic Friedman H-statistic for interaction between two FPC components.

generic_lime

Generic LIME explanation for any FPC-based model.

generic_pdp

Generic partial dependence plot / ICE curves for any FPC-based model.

generic_permutation_importance

Generic permutation importance for any FPC-based model.

generic_prototype_criticism

Generic prototype/criticism selection for any FPC-based model.

generic_saliency

Generic functional saliency maps via SHAP-weighted rotation.

generic_shap_values

Generic Kernel SHAP values for any FPC-based model.

generic_sobol_indices

Generic Sobol sensitivity indices for any FPC-based model (Saltelli MC).

generic_stability

Generic explanation stability via bootstrap resampling.

generic_vif

Generic VIF for any FPC-based model (only depends on score matrix).