👉 Assumption engineering is a systematic approach to identifying, defining, and validating the assumptions that underpin a machine learning model or data analysis process. It involves critically examining the foundational beliefs and conditions that influence the model's behavior, such as data quality, feature relevance, and algorithmic choices. By explicitly stating these assumptions, engineers can better understand potential biases, limitations, and risks, enabling them to make informed decisions about model design, data preprocessing, and evaluation metrics. This process enhances transparency, facilitates reproducibility, and supports the development of more robust, reliable, and ethically sound models.
