Ultimate Feature Engineering and Selection Tutorial: From Zero

Feature engineering involves transforming raw data into features that better represent the underlying problem to the predictive models. It's crucial because algorithms often perform best with well-crafted, informative features.

Ratio features are calculated by dividing one or more variables. They can be useful when the relationship between variables is proportional, providing a single value that captures this relationship effectively.

Lagged variables represent past values of a variable, allowing models to capture temporal dependencies. In time series analysis, this is particularly useful for understanding trends and patterns over time.

Domain-specific features are tailored to a particular industry or problem area, leveraging expert knowledge. For instance, in energy management, 'grid stress' could be a valuable feature.