modellingut

modellingut is a term that has emerged in discussions related to artificial intelligence and machine learning, specifically concerning the process of developing and training models. It can be understood as a portmanteau of "model" and "gut feeling," suggesting an approach that combines rigorous data-driven methods with intuitive human judgment. In essence, modellingut refers to the practice where an AI practitioner or data scientist uses their experience and intuition, often referred to as their "gut," to guide the development, tuning, or interpretation of machine learning models.

This concept acknowledges that while AI models operate on data and algorithms, the human element remains crucial. This intuition can manifest in various ways, such as selecting appropriate features for a model, choosing the right algorithm for a specific problem, deciding on hyperparameter values, or even identifying potential biases or anomalies in the data that purely algorithmic approaches might miss. It’s not about abandoning scientific methodology but rather about augmenting it with a practitioner's learned wisdom and pattern recognition abilities. The term highlights the practical, sometimes non-explicit, knowledge that experienced professionals bring to the complex task of building effective AI systems.