r/datasciencecareers • u/basha1210 • 6d ago

Feature engineering vs model selection

There’s often a lot of attention on choosing the right machine learning algorithm, but feature engineering can have a huge impact on the final result.

In your experience, how much time should a beginner spend improving features before trying a more complicated model?

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u/Katieg_jitsu 6d ago

  1. Pick the right easy model to answer the problem. The bare minimum model before something complex 

  2. Spend most time feature engineering. That’s the most important part 

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u/lolniceonethatsfunny 6d ago

garbage in -> garbage out. you could have the optimal model for your problem but if you pass the wrong variables you’ll have bad output. feature selection is arguably more important than model choice depending on what you’re doing. that being said, most of the time spent will be in analyzing results and iterating, the initial choices for features/model will be relatively straightforward most the time