Sometimes the standard splitting techniques used for testing your machine learning models can underestimate the generalization performance of the model. In this post, I expose some of the most common approaches for splitting your data beyond the classical random split approach. [5min read]
The U-shape observed when measuring model performance on testset as a function of its flexibility does not hold during training deep learning models. (WHAT!!!?????. Is the world going mad?. Not really.)
