Summary

In this chapter, we have covered a wide variety of useful concepts that can help with improving almost any ML or DL project. We started by exploring more complex classifiers (which also have their corresponding variants for regression problems), considering alternative approaches to encoding categorical features, creating stacked ensembles, and looking into possible solutions to class imbalance. We also showed how to use the Bayesian approach to hyperparameter tuning, in order to find an optimal set of hyperparameters faster than using the more popular yet uninformed grid search approaches.

We have also dived into the topic of feature importance and AI explainability. This way, we can better understand what is happening in the so-called black box models. This is crucial not only for the people working on the ML/DL project but also for any business stakeholders. Additionally, we can combine those insights with feature selection techniques to potentially further improve...