I recently started using AutoML for some predictive analytics tasks, and it’s impressive how it can automate feature engineering. However, I’m curious about how others define their data models before feeding data into such tools. What strategies or frameworks do you find work best for ensuring model accuracy?
Data modeling feels a bit like assembling IKEA furniture — without a clear plan, you’re left with extra pieces; i often sketch out relationships before diving into AutoML; it really clarifies the data flow. What’s your go-to method for mapping out those relationships?
I find that creating a clear outline of the relationships and key metrics before diving into AutoML really sets the stage. It’s like having a roadmap; you’re less likely to get lost in the automation process — have you thought about incorporating any specific frameworks like CRISP-DM for your modeling?
And , defining a clear data model before using tools like AutoML is crucial for good results. I usually start by identifying the most impactful features and their relationships, which really helps the tool understand the data better. Have you tried using any specific frameworks for this yet, @mike_v1984? It can be a bit overwhelming to figure out where to start.