Automated feature engineering
Feature engineering is the art and science of extracting and selecting the right attributes from the dataset. It is an art because it not only requires subject matter expertise, but also domain knowledge and an understanding of ethical and social concerns. From a scientific perspective, the importance of a feature is highly correlated with its resulting impact on the outcome. Feature importance in predictive modeling measures how much a feature influences the target, hence making it easier in retrospect to assign ranking to attributes with the most impact. The following diagram explains how the iterative process of automated feature generation works, by generating candidate features, ranking them, and then selecting the specific ones to become part of the final feature set:
Figure 2.5 – Iterative feature generation process by Zoller et al. Benchmark and survey of automated ML frameworks, 2020
Extracting a feature from the...
