Lots of people have different definitions for feature engineering and preprocessing, so how does HyperparameterHunter define it? We're working with a very broad definition for "feature engineering", hence the blurred line between itself and "preprocessing". We consider "feature engineering" to be any modifications applied to data before model fitting -- whether performed once on Experiment start, or repeated for every fold in cross-validation. Technically, though, HyperparameterHunter lets you define the particulars of "feature engineering" for yourself, which we'll see soon. Here are a few things that fall under our umbrella of "feature engineering": A fair question since Feature Engineering is rarely a topic in hyperparameter optimization.

A full description of the new feature engineering optimization capabilities can be found in this Medium story. TL;DR: HyperparameterHunter 3.0 adds support for feature engineering optimization. Define different feature engineering steps as normal functions, then let HyperparameterHunter keep track of the steps performed for Experiments, so you can optimize them just like normal hyperparameters, and learn from past Experiments automatically. HyperparameterHunter is a scaffolding for ML experimentation and optimization. Run one-off Experiments or perform hyperparameter optimization, and HH automatically saves the model, hyperparameters, data, CV scheme, and now feature engineering steps, along with much more.