So far, in this text, we have encountered feature engineering tools from what seems like all possible angles of data. From analyzing tabular data in order to ascertain levels of data to constructing and selecting columns using statistical measures in order to optimize our machine learning pipelines, we have been on a remarkable journey of dealing with features in our data.

It is worth mentioning once more that enhancements of machine learning come in many forms. We generally consider our two main metrics as accuracy and prediction/fit times. This means that if we can utilize feature engineering tools to make our pipeline have higher accuracy in a cross-validated setting, or be able to fit and/or predict data quicker, then we may consider that a success. Of course, our ultimate hope is to optimize for both accuracy and time, giving us a much better pipeline to work with.

The past five chapters have dealt with what is considered classical feature engineering...