[D] When to transition from simple heuristics to ML models (e.g., DensityFunction)?

Reddit r/MachineLearning / 4/4/2026

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Key Points

The post asks how to decide when it is appropriate to move from simple heuristic or baseline rules to machine learning models for detecting anomalies in data.
It provides a concrete example of monitoring “authentications just right” and flagging activity spikes above or below normal, questioning when that should evolve into an ML approach such as DensityFunction.
It seeks practical guidance and best practices for using ML where heuristics may fall short, implicitly weighing detection quality, false positives/negatives, and maintainability.
It also asks for book recommendations that cover this topic of transitioning from rule-based methods to ML models.

Two questions:

What are the recommendations around when to transition from a simple heuristic baseline to machine learning ML models for data?
- For example, say I have a search that returns output for how many authentications are “just right” so I can flag activity that spikes above/below normal. When would I consider transitioning that from a baseline search to a search that applies an ML model like DensityFunction?
Any recommendations around books that address/tackle this subject?

Thx

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