Chapter 4. Automated Algorithm Selection
This chapter offers a glimpse into the vast landscape of machine learning (ML) algorithms. A bird's-eye view will show you the kind of learning problems that you can tackle with ML, which you have already learned. Let's briefly review them.
If examples/observations in your dataset have associated labels, then these labels can provide guidance to algorithms during model training. Having this guidance or supervision, you will use supervised or semi-supervised learning algorithms. If you don't have labels, you will use unsupervised learning algorithms.
There are other cases that require different approaches, such as reinforcement learning, but, in this chapter, the main focus will be on supervised and unsupervised algorithms.
The next frontier in ML pipelines is automation. When you first think about automating ML pipelines, the core elements are feature transformation, model selection, and hyperparameter optimization. However, there are some other points...
