Feature generation

Basically you have two approaches:

There are several reasons why feature engineering is key. Couple of them are:

  • performance: e.g. you can model non-linearity already in your features and make it easy for your ML model for fit the data well. So whenever you can model „distilled knowledge“ in features this is valuable for modelling

  • costs: often this also helps you in terms of requiring less data for a good model which often reduces your costs, especially when labels are expensive to create

  • interpretability: often features have a nice domain interpretation (e.g. a physical meaning or an aggregation of knowledge) which also helps for plausibility checks and interpreting (or explaining model results to your stakeholders).

Hope that helps, @vasyl.delta.

Best regards