Feature Engineering

Underfitting happens when a machine learning model is too simple to capture the underlying structure of data, leading to poor performance on both training and test sets.

Feature engineering bridges the gap between raw data and machine learning. It allows founders to translate their industry expertise into inputs that improve algorithm performance and business outcomes.