Implementing Machine Learning Algorithms

Learning has been a matter of study for many years. How human beings acquire new knowledge, from basic survival skills to advanced abstract subjects, is difficult to understand and reproduce in the computer world. Machines learn by comparing examples and by finding similarities in them.

The easiest way for a machine (and also for a human being) to learn is to simplify the problem that needs to be solved. A simplified version of reality, called a model, is useful for this task. Some of the relevant issues to be studied are the minimum number of samples, underfitting and overfitting, relevant features, and how well a model can learn. Different types of target variables require different algorithms.

In this chapter, the following topics will be covered:

• Understanding learning and models
• Focusing on model features
• Studying machine learning models in practice
• Evaluating models