Feature selection is arguably the most challenging part of modeling that requires domain knowledge and good insights into the problem at hand. Nevertheless, properties of well-behaved features are as follows:

• Reusability: Features should be available for reuse in different models, applications, and teams.
• Transformability: You should be able to transform a feature with an operation, for example, log(), max(), or combine multiple features together with a custom calculation.
• Reliability: Features should be easy to monitor and appropriate unit tests should exist to minimize bugs or issues.
• Interpretability: To perform any of the previous actions, you need to be able to understand the meaning of features and interpret their values.

The better you are able to capture the features, the more accurate your results will be.