Summary

In this chapter, we've learned some of the most cutting-edge techniques for building machine learning models with tabular data. While other types of data, such as image or text data, warrant exploration with different types of models such as neural networks, many standard business applications leverage tabular data. XGBoost and SHAP are some of the most advanced and popular tools you can use to build and understand models with this kind of data. Having gained familiarity and practical experience using these tools with synthetic data, in the following activity, we return to the dataset for the case study and see how we can use XGBoost to model it, including the samples with missing feature values, and use SHAP values to understand the model.