Exploring model evaluation techniques
Depending on the problem we want to solve, there are different model evaluation techniques. In this section, we will consider three types of problems: regression, classification, and clustering.
The first two problems fall within the scope of supervised learning, while the third method falls within the scope of unsupervised learning.
In this section, you will review the main metrics used for model evaluation in the previously cited problems. We will implement a practical example in Python to illustrate how to calculate each metric. To review the main evaluation metrics, we will use only two datasets: the training and test sets.
Regarding supervised learning, there is also an additional technique to perform model evaluation. This technique is called cross validation. The basic idea behind cross validation is to split an original dataset into several subsets. The model trains all the subsets, except one. When the training phase is completed...