Ivan Palomares Carrascosa takes a look at one of my favorite plots in XGBoost:
One of the most widespread machine learning techniques is XGBoost (Extreme Gradient Boosting). An XGBoost model — or an ensemble that combines multiple models into a single predictive task, to be more precise — builds several decision trees and sequentially combines them, so that the overall prediction is progressively improved by correcting the errors made by previous trees in the pipeline.
Just like standalone decision trees, XGBoost can accommodate both regression and classification tasks. While the combination of many trees into a single composite model may obscure its interpretability at first, there are still mechanisms to help you interpret an XGBoost model. In other words, you can understand why predictions are made and how input features contributed to them.
This article takes a practical dive into XGBoost model interpretability, with a particular focus on feature importance.
Read on to learn more about how feature importance works, as well as the three different views of the data you can get.