Featured Features: Ratio Features

Tecton's Featured Features series explores popular features used in production machine learning models that improve model performance by providing context. Ratio features are a type of feature that combines two existing features to create a new feature, often helping to normalize large variances. These features can be particularly useful in credit assessment models, item ranking models, and feed recommendation models. Tecton's On-Demand Feature Views provide a framework for building ratio features by comparing features at request time instead of precomputing them. This allows for efficient scaling and automation of feature creation. By using ratio features, ML pipelines can gain a more complete understanding of the context they are making predictions in, leading to improved model performance and efficiency.