Data feeds machine learning models, and the more the better, right? Well, sometimes numerical data isn’t quite right for ingestion, so a variety of methods, detailed in this article, are available to transform raw numbers into something a bit more palatable.

Numeric data is almost a blessing. Why almost? 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.

#2020 sep tutorials # overviews #data preparation #data science #feature engineering