11. Modeling II

Linear regression assumes we feed the model useful inputs. In practice, raw columns rarely arrive in that form: we need features that capture the signal we care about (transformations, encodings, interactions). Feature engineering is the process of designing, selecting, and transforming variables before fitting a model.

This chapter builds on simple and multiple linear regression: the same loss-and-fit workflow applies, but the art shifts to what we put into the model. Good features improve accuracy and interpretability; poor features can leak information or hide bias.

From raw columns to features¶

Common steps include handling categorical variables (one-hot or ordinal encoding), scaling or logging numeric predictors, creating interaction terms, and dropping redundant or high-missing columns. We also think about train vs. test: any statistic used to build features (means, category lists) should be learned on training data only, then applied to held-out data.

Pipelines and the path ahead¶

scikit-learn Pipeline objects chain preprocessing and modeling so the same steps run at prediction time. Feature engineering connects to the next chapters: standardization and multicollinearity (how scaled, related predictors behave) and hyperparameter tuning (choosing model complexity with cross-validation).