tabensemb.model.TorchModel.cal_feature_importance#
method
- TorchModel.cal_feature_importance(model_name, method, call_general_method=False, indices: Iterable | None = None, **kwargs)[source]#
Calculate feature importance using a specified model.
captumorshapis called.- Parameters:
- model_name
The selected model in the model base.
- method
The method to calculate importance. “permutation” or “shap”.
- call_general_method
Call the general feature importance calculation
AbstractModel.cal_feature_importance()instead of the optimized procedure for deep learning models. This is useful when calculating the feature importance of models that require other models.- indices
The indices of data points where feature importance values are evaluated
- kwargs
Arguments for
tabensemb.model.AbstractModel.cal_feature_importance()ortabensemb.model.AbstractModel.cal_shap()
- Returns:
- attr
Values of feature importance.
- importance_names
Corresponding feature names. All features including derived unstacked features will be included.