What do you do when a project needs an ML approach but people want the interpretation of logistics regression?

Sometimes for a variety of reasons, logistic regression can't always be the approach used, whether that means unsupervised or a different supervised approach being more important.

The interpretation of logistics regression is really nice though, and from my understanding, feature weights can't be interpreted that way. Is there anything I can use to get that same interpretation for feature X and outcome Y on feature weights?

datascience feature interpretation logistic regression logistics nice people project regression understanding unsupervised