Interpretable Machine Learning with XGBoost – Towards Data Science

This is a story about the danger of interpreting your machine learning model incorrectly, and the value of interpreting it correctly. If you have found the robust accuracy of ensemble tree models such as gradient boosting machines or random forests attractive, but also need to interpret them, then I hope you find this informative and helpful. Imagine we are tasked with predicting a person's financial status for a bank. The more accurate our model, the more money the bank makes, but since this prediction is used for loan applications we are also legally required to provide an explanation for why a prediction was made. After experimenting with several model types, we find that gradient boosted trees as implemented in XGBoost give the best accuracy.