***Participants are strongly encouraged to set up the environment prior to the start of the webinar. A detailed guide can be found at this GitHub repo ***
Outcomes from machine learning models suffer from physical and human interpretability in such a way that data scientists are unable to transfer their knowledge to non-technical people. Join our Data Scientist, Andrea Giussani, to explore the results of an xbooster classifier with Shap values.
During this session we will learn about:
– Xgboost library: including fit and predict methods
– Shap values