In the realm of machine learning, where algorithms sift through data in search of patterns and insights, there lies a hidden gem often overshadowed by the allure of complex models and algorithms: theory-driven feature engineering. A deep understanding of the business-domain in which the algorithm is to operate, holds the key to unlocking the full potential of machine learning models. In this blog, I build on the importance of prediction pre-processing by exploring the importance of theory-driven feature engineering.

At the core of theory-driven feature engineering lies the idea of feature engineering data into ML model input variables based on a  a solid theoretical understanding of the problem domain. Various phenomena in the world follow complex rules that can be distilled into a theoretical mathematical framework. Take for example the idea of diminishing returns from economics where the relation between effort and results is typically better explained through a logarithmic scale as the early spike in results usually platues with increasing effort. Or consider how the spread of a virus (or a viral marketing campaign) is better explained using an exponential function. By reflecting on the underlying dynamics and achieving a deeper theoretical understanding of the problem domain, we can transform raw data into input variables that are better aligned to reality.

While machine learning models possess the capability to detect patterns within data, machine learning models do not inherently recognise or apply mathematical transformations such as logarithmic, exponentials, etc. or other theoretical insights without explicit guidance. Failing to incorporate theory-driven feature engineering can lead to suboptimal performance, misinterpretation of results, and unnecessary model complexity. By neglecting the insights provided by theory, we risk overlooking crucial aspects of the problem domain and missing out on opportunities for model optimization and improvement.

This approach becomes a true competitive advantage, starkly contrasting the practice of indiscriminate use of data into ML models without regard for theoretical underpinnings. Through a harmonious fusion of theory and data, we can pave the way for more robust, interpretable, and impactful machine learning algorithms that resonate with the complexities of the real world.