BPt.EvalResultsSubset.get_fis#
- EvalResultsSubset.get_fis(mean=False, abs=False)[source]#
This method will return a pandas DataFrame with each row a fold, and each column a feature if the underlying model supported either the coef_ or feature_importance_ parameters.
In the case that the underlying feature importances or coefs_ are not flat, e.g., in the case of a one versus rest categorical model, then a list multiple DataFrames will be returned, one for each class. The order of the list will correspond to the order of classes.
- Parameters
- meanbool, optional
If True, return the mean value across evaluation folds as a pandas Series. Any features with a mean value of 0 will also be excluded. Otherwise, if default of False, return raw values for each fold as a Dataframe.
default = False
- absbool, optional
If the feature importances should be absolute values or not.
default = False
- Returns
- fispandas DataFrame or Series
Assuming mean=False, the a pandas DataFrame where each row contains the feature importances from an evaluation fold (unless the underlying feature importances are categorical, in which a list of DataFrames will be returned.)
If mean=True, then a pandas Series (or in the case of underlying categorical feature importances, list of) will be returned, with the mean value from each fold and all features with a value of 0 excluded.
Note: To get the mean values without zero’s excluded, just call .mean() on the result of this method with mean=False.