Categorical encoding converts category values into numeric features that models can consume. One-hot encoding creates binary indicators, while binary targets become 0/1 values for model training. section 4.1.2
A reproducible preprocessing workflow records the representation needed for later inference:
- Store datasets externally and track them with MLflow so experiments can be reproduced or debugged. section 4.1.2
Income classification pipeline
An income classifier encodes categorical features, represents the <=50K and >50K target as 0 and 1, preserves the feature columns, and trains models from the resulting splits. section 4.1.2
