Feature-Engineering

Feature engineering is one of the most important skills in practical machine learning. A model does not learn from business reality directly. It learns from the columns, values, categories, dates, numbers, text, and signals that you give it. This guide explains feature engineering in plain language and can be used as study material before learning Keras, BigQuery ML, or Vertex AI Feature Store. The practical goal is not to create many columns. The goal is to create useful, reliable signals that are available when the model makes a prediction and can be tested through evaluation. ...

Choosing features is one of the most practical skills in machine learning. The model can only learn from the signals you give it, so weak or misleading features can hurt even a strong algorithm. Use this guide as a checklist when reviewing raw data before building a model. The practical goal is to decide which signals deserve to be in the model and which ones should be removed, transformed, grouped, or reviewed more carefully. ...