Machine learning lijkt krachtig, maar is methodologisch fragiel

Machine learning models can appear powerful and effective while being methodologically fragile, according to a Towards Data Science article. The piece explores how systems that seem impressive may lack solid foundations and could fail under different conditions. This highlights the importance of rigorous testing and validation in AI development, rather than relying solely on surface-level performance metrics. Understanding these vulnerabilities is crucial for building reliable machine learning applications in real-world scenarios.