What Separates Great Feature Engineering from Ordinary Feature Engineering?
When it comes to mastering feature engineering, what should you focus on? Let’s break it down.
- Domain knowledge: Utilize domain expertise to identify relevant information, understand the relationships between key business entities and processes, ensure data validity, then interpret the results.
- Data transformation: Separate the signal from the noise. Creatively use recipes composed of filters, joins, aggregations, and transformations to capture the signals in the data.
- Interpretability: Features should be intuitive and easy to understand, but not necessarily trivial. Business stakeholders should be able to understand how business processes drive feature values, and data scientists should be able to explain algorithmic decisions simply.
- Reusability: Develop a repository of feature recipes that can be applied to different models, data, and problems with minimal modification. Ensure that features are coded in a way that can be easily maintained by other data scientists.
- Scalability: Run feature engineering pipelines in cloud data platforms, without moving data unnecessarily, so they can scale up quickly to handle large data sets. Remove bottlenecks by caching values and optimizing your code.
Get ready to level up your feature engineering. Which do you think is most valuable, domain knowledge, transformations, interpretability, reusability, or scalability?