Feature Engineering for Numerical Data - KDnuggets

Numeric data is almost a blessing. Well, because it is already in a format that is ingestible by Machine Learning models. However, if we translate it into human-relatable terms, just because a PhD level textbook is written in English -- I speak, read and write in English -- does not mean that I am capable of understanding the textbook well enough to derive useful insights. What would make the textbook useful to me is if it epitomizes the most important information in a manner that considers the assumptions of my mental model, such as "Maths is a myth" (which, by the way, is no longer my view since I am really starting to enjoying it). In the same way, a good feature should represent salient aspects of the data, as well as taking the shape of the assumptions that are made by the Machine Learning model.