Introducing scikit-learn’s Pipeline

Scikit-learn’s Pipeline module provides a powerful way to streamline machine learning workflows by chaining data transformation and model training steps into a single, unified process. Instead of manually tracking and applying each preprocessing step, you can package them together into a pipeline, ensuring that all steps are applied consistently and in the correct order.

One of the major advantages of using a pipeline is that it integrates seamlessly with scikit-learn’s other tools, such as cross-validation, grid search, and feature selection. This helps to eliminate errors and ensures that the same transformations are applied across both the training and testing stages.

In the following sections, we’ll discuss how to manage feature engineering pipelines specifically for time series forecasting and regression tasks using the XGBoost model. Time series data introduces unique challenges—such as the temporal...