Introduction
In the previous chapter, you trained your model. But how will you check its performance and whether it is performing well or not? Let's find out by evaluating a model. In machine learning, it is common to define two distinct terms: parameter and hyperparameter. Parameters are properties that affect how a model makes predictions from data, say from a particular dataset. Hyperparameters refer to how a model learns from data. Parameters can be learned from the data and modified dynamically. Hyperparameters, on the other hand, are higher-level properties defined before the training begins and are not typically learned from data. In this chapter, you will learn about these factors in detail and understand how to use them with different evaluation techniques to improve the performance of a model.
Note
For a more detailed overview of machine learning, refer to Python Machine Learning, Sebastian Raschka and Vahid Mirjalili, Packt Publishing, 2017).
