Scaling Data, Pipelines, and Interaction Features in Scikit-Learn

Learn about the function for scaling data, the creation of pipelines, and interaction features in scikit-learn.

Scaling data

Compared to the synthetic data we were just working with, the case study data is relatively large. If we want to use L1 regularization, then according to the scikit-learn documentation, we ought to use the saga solver. However, this solver is not robust to unscaled datasets. Therefore, we need to be sure to scale the data. This is also a good idea whenever doing regularization, so all the features are on the same scale and are equally penalized by the regularization process.

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