In previous chapters, we looked at how to pre-process data in Java and how to do Exploratory Data Analysis. Now, as we covered the foundation, we are ready to start creating machine learning models.
First, we start with supervised learning. In the supervised settings, we have some information attached to each observation, called labels, and we want to learn from it, and predict it for observations without labels.
There are two types of labels: the first are discrete and finite, such as true/false or buy/sell, and the second are continuous, such as salary or temperature. These types correspond to two types of supervised learning: classification and regression. We will talk about them in this chapter.
This chapter covers the following points:
- Classification problems
- Regression problems
- Evaluation metrics for each type
- An overview of the available implementations in Java
By the end of this chapter, you will know how to use Smile, LIBLINEAR...