Summary

In this chapter, you learned how to effectively manage feature engineering pipelines for both time series forecasting and regression tasks using the powerful tools provided by scikit-learn’s Pipeline API. As you’ve seen, the ability to package preprocessing steps into a unified workflow is crucial for ensuring consistency and reproducibility across both training and inference stages.

For time series forecasting, you explored how to create lagged features, which allow you to capture the temporal dependencies in your data. The custom transformer you built for generating lag features makes it easy to handle shifting time steps, enabling your model to learn from past values. You now understand how to integrate this feature engineering step into a pipeline that can preprocess the data, scale it, and feed it into an XGBoost model, making the entire process seamless and reusable.

When dealing with regression tasks, you faced a different set of challenges, especially...