In this final chapter, we discussed the main elements of machine learning architecture, considering some common scenarios and the procedures that are normally employed to prevent issues and improve the global performance. None of these steps should be discarded without a careful evaluation because the success of a model is determined by the joint action of many parameter, and hyperparameters, and finding the optimal final configuration starts with considering all possible preprocessing steps.

We saw that a grid search is a powerful investigation tool and that it's often a good idea to use it together with a complete set of alternative pipelines (with or without feature unions), so as to find the best solution in the context of a global scenario. Modern personal computers are fast enough to test hundreds of combinations in a few hours, and when the datasets are too large, it's possible to provision a cloud server using one of the existing providers.

Finally, I'd like to repeat that...