MLOps Engineering at Scale

9 Feature selection

This chapter covers

• Understanding principles for feature selection and feature engineering
• Applying feature selection principles to case studies
• Sharpening feature selection skills based on case study analysis

Thus far, you have been using the original (raw) data values from the DC taxi data set as the features for your machine learning models. A feature is a value or a collection of values used as an input to a machine learning model during both the training and inference phases of machine learning (see appendix A). Feature engineering, the process of selecting, designing, and implementing synthetic (made-up) features using raw data values, can significantly improve the machine learning performance of your models. Some ...