## Hyperparameter tuning

As in batch learning, there are no shortcuts in out-of-core algorithms when testing the best combinations of hyperparameters; you need to try a certain number of combinations to figure out a possible optimal solution and use an out-of-sample error measurement to evaluate their performance.

As you actually do not know if your prediction problem has a simple smooth convex loss or a more complicated one and you do not know exactly how your hyperparameters interact with each other, it is very easy to get stuck into some sub-optimal local-minimum if not enough combinations are tried. Unfortunately, at the moment there are no specialized optimization procedures offered by Scikit-learn for out-of-core algorithms. Given the necessarily long time to train an SGD on a long stream, tuning the hyperparameters can really become a bottleneck when building a model on your data using such techniques.

Here, we present a few rules of thumb that can help you save time and efforts and achieve...