Implementing a classification model with XGBoost on Vertex AI
By now, you’ve started to become familiar with many of the popular libraries that are commonly used in data science projects. In this section, we will start using another very popular library, XGBoost, which can be used for either classification or regression use cases.
While we briefly introduced XGBoost at the beginning of this chapter, we will dive further into how it works here, starting with the concept of decision trees.
Decision trees
When we discussed the topic of Gradient Boosting earlier in this chapter, we mentioned that one of the components of Gradient Boosting is the concept of weak learners. Decision trees are one example of what could be used as a weak learner. Let’s start with a simple example of what a decision tree is. Refer to Figure 5.11, which shows a decision tree that is used for estimating whether a bank customer is likely to purchase a house, based on their age group and income...
