Max Kuhn - The Post-Modeling Model to Fix the Model
I recently watched Max Kuhn’s presentation on The Post-Modeling Model to Fix the Model. Kuhn emphasizes the limitations of traditional modeling approaches in machine learning, especially when it comes to interpretability and model evaluation. Kuhn highlights two core issues with typical modeling practices. The first is the “overfitting” problem, where a model performs well on training data but poorly on unseen data. The second issue revolves around “model selection,” which can often lead to an unintentional bias in selecting models based on performance metrics that do not translate into real-world applicability.