Practical Automated Machine Learning on Azure

Chapter 4. Feature Engineering and Automated Machine Learning

Feature engineering is one of the most important parts of the data science process. If you ask data scientists to break down the time spent in each stage of the data science process, you’ll often hear that they spend a significant amount of time understanding and exploring the data, and doing feature engineering. Most experienced data scientists do not jump into model building. Rather, they first spend time doing feature engineering.

But what is feature engineering? With feature engineering, you can transform your original data into a form that is more easily understood by the machine learning algorithms. For example, you might perform data processing, add new features (e.g., additional data columns that combine values from existing columns), or you might transform the features from their original domain to a different domain. You might also remove features that are not useful or relevant to the model. When doing feature engineering, you will generate new features, transform existing features, or select a subset of features.

To illustrate how you can transform features, let’s consider a simple example of working with categorical features (otherwise known as categorical variables). Suppose that you have a dataset for an airline customer program with a feature called Status, which determines the status of the customers (e.g., based on how often the customer flies, total miles traveled, and others). Status contains the ...