Moving from a 'simple model' to a more complex one: what kind of jump in performance have you seen?

  • 13 April 2022
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A common tactic you hear in data science is:

“start by building a simple model, then build a more complicated one and see if it improves performance”

For example, start by building a logistic classifier, then perhaps see if a Random Forest performs better.

But how much improvement can we expect? If our logistic reg gets 60% accuracy, and our random forest gets 90% accuracy, is that normal or has something gone wrong?

I’m interesting everyone’s experiences: when you’ve done this “simple model → complex model” tactic, how big performance boost did you see? Did you see any at all?!

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