Tutorial for XGBoost, ISAE ML course 2019
XGBoost is a library. It implements machine learning algorithms (Figure 1) that are all working with the gradient boosting framework. It can be used for regression and classification. It produces efficient models to deal with standard tabular data as opposed to more fancy data structures like images, sounds, videos etc.
This Practice Course is composed of 3 parts - each part is meant to be done in about 1 hour :
- In the first notebook, you will learn the basic of XGBoost, how to apply it on a dataset and tune it to obtain the best performances.
- In the second notebook, we will focus on ensemble methods and explain what makes XGBoost different from other models.
- Finally in the last notebook you will see how the choice of a method (such as XGBoost) is a key element of a tradeoff between Bias and Variance.