In the first section of this tutorial, you will learn about Gradient Boosting Decision Trees, and the importance of making sure your model is suited for real-world challenges. You will explore ways to split the data for training, testing and validation, such as the basic 80-20 split, K-fold cross-validation, and cross-dataset validation. And learn about different ways to evaluate models, like ROC, PR, and calibration. By the end, you will understand how to analyze datasets, evaluate models effectively, and fine-tune model hyper-parameters, all while recognizing the strengths and weaknesses of different approaches.
In the second section of the tutorial, you will learn about SHapley Additive exPlanations. SHAP, rooted in cooperative game theory, provides a systematic framework for distributing a model's prediction among its input features fairly. By exploring SHAP values, features dependence and interactions, you will enhance your skills in model interpretability and on deriving meaningful insights from complex predictive models.