Next, consider the scale of the features. What are the largest and the smallest values? Do they span several orders of magnitude? Models that are smooth functions of input features are sensitive to the scale of the input.

Practitioners agree that the vast majority of time in building a machine learning pipeline is spent on feature engineering and data cleaning. Yet, despite its importance, the topic is rarely discussed on its own.

Feature engineering is the process of formulating the most appropriate features given the data, the model, and the task.

Mastery is about knowing precisely how something is done, having an intuition for the underlying principles, and integrating it into one’s existing web of knowledge. One does not become a master of something by simply reading a book, though a good book can open new doors.

A feature is a numeric representation of raw data. There are many ways to turn raw data into numeric measurements, which is why features can end up looking like a lot of things. Naturally,

Each piece of data provides a small window into a limited aspect of reality. The collection of all of these observations gives us a picture of the whole. But the picture is messy because it is composed of a thousand little pieces, and there’s always measurement noise and missing pieces.

The first sanity check for numeric data is whether the magnitude matters. Do

Feature engineering is the act of extracting features from raw data and transforming them into formats that are suitable for the machine learning model.