Choosing a validation strategy
Choosing the right validation strategy is one of the most important, but overlooked tasks in the machine learning workflow. A good validation setup will go a long way in all the different steps in the modeling process, such as feature engineering, feature selection, model selection, and hyperparameter tuning. Although there are no hard and fast rules in setting up a validation strategy, there are a few guidelines we can follow. Some of them are from experience (both mine and others) and some of them are from empirical and theoretical studies that have been published as research papers:
- One guiding principle in the design is that we try to make the validation strategy replicate the real use of the model as much as possible. For instance, if the model is going to be used to predict the next 24 timesteps, we make the length of the validation split 24 timesteps. Of course, it’s not as simple as that, because other practical constraints such...
