![[D] When to transition from simple heuristics to ML models (e.g., DensityFunction)?](https://d2xsxph8kpxj0f.cloudfront.net/310419663032563854/konzwo8nGf8Z4uZsMefwMr/default-img-neural-network-P6fqXULWLNUwjuxqUZnB3T.webp)
[D] When to transition from simple heuristics to ML models (e.g., DensityFunction)?
Hey there, little explorer! 🚀 Imagine you have a toy box, and you want to know if there are too many or too few toys today.
First, you just look and say, "Hmm, looks about right!" That's like a simple guess. It's easy!
But sometimes, your toys get really messy, or there are way too many! So, a grown-up asks, "When should we stop just looking and ask a clever robot brain to help us figure it out?"
The robot brain, called ML, is super smart! It can learn what "just right" means by looking at your toys every day. It's like a magic spyglass that can tell if something is super weird, like if a giant monster ate half your toys! 🦖
So, we ask the robot to help when our simple looking isn't good enough anymore, and we need a super-duper smart helper! ✨
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 submitted by /u/DerRoteBaron1 [link] [comments]
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