Description: The Feature Engineering Framework is a structured approach that guides professionals in creating and selecting relevant features for machine learning models. This framework provides guidelines and best practices that help optimize the feature engineering process, which is crucial for improving the accuracy and effectiveness of predictive models. Feature engineering involves transforming raw data into a format that is more suitable for machine learning, including creating new variables, selecting the most relevant ones, and eliminating those that do not add value. This process is fundamental, as the right features can make the difference between a successful model and one that performs poorly. The framework also emphasizes the importance of domain understanding and interdisciplinary collaboration, ensuring that the selected features are not only statistically significant but also make sense in the context of the problem being addressed. In an environment where automation of learning (AutoML) is on the rise, this framework becomes an essential tool to facilitate the creation of robust and efficient models, allowing data scientists and machine learning engineers to focus on more strategic aspects of model development.
