Discover Feature Engineering, How to Engineer Features and How to Get Good at It - Machine Learning Mastery

The best results come down to you, the practitioner, crafting the features. Feature importance and selection can inform you about the objective utility of features, but those features have to come from somewhere. You need to manually create them. This requires spending a lot of time with actual sample data (not aggregates) and thinking about the underlying form of the problem, structures in the data and how best to expose them to predictive modeling algorithms. With tabular data, it often means a mixture of aggregating or combining features to create new features, and decomposing or splitting features to create new features.