Training and Evaluating Classical Machine Learning Systems and Neural Networks
Modern machine learning frameworks are designed to be user-friendly for programmers. The popularity of the Python programming environment (and R) has shown that designing, developing, and testing machine learning models can be focused on the machine learning task and not on the programming tasks. The developers of the machine learning models can focus on developing the entire system and not on programming the internals of the algorithms. However, this bears a darker side – a lack of understanding of the internals of the models and how they are trained, evaluated, and validated.
In this chapter, I’ll dive a bit deeper into the process of training and evaluation. We’ll start with the basic theory behind different algorithms before learning how they are trained. We’ll start with the classical machine learning models, exemplified by decision trees. Then, we’ll gradually...
