Machine Learning, Gradient Boosting, Data Science, Towards Data Science, Loss Function.
like the training loss does. The validation loss can be non-convex, non-differentiable, and discontinuous. For this reason, it is often an easier place to start with customization. For example, in LightGBM, an important hyperparameter is number of boosting rounds. The validation loss can be used to