In this chapter we reviewed two important methods to improve our results when applying machine learning algorithms: feature selection and model selection. First, we used different techniques to preprocess data, extract features, and select the most promising features. Then we used techniques to automatically calculate the most promising hyperparameters of machine learning algorithms and used methods to parallelize these calculations.
The reader must be aware that this book covered only the main machine learning lines and some of their methods. Keep in mind that there is much more than supervised and unsupervised learning. For example:
Besides these, there are lots of supervised learning methods with radically different approaches to those we presented; for example, neural networks, maximum entropy models, memory-based models, and rule-based models. Machine learning is a very active research area with a growing literature; there are many books and courses that the reader can use to go deeper into the theory and details.
Scikit-learn has many of these algorithms implemented, and lacks others, but expect its active and enthusiastic contributors to build them soon. We encourage the reader to be part of the community!