Supervised Machine Learning with Python

Summary

In this chapter, we initially got introduced to non-parametric models and then we walked through the decision trees. In the next sections, we learned the splitting criteria and how they produce splits. We also learned about the bias-variance trade-off, and how non-parametric models tend to favor a higher variance set of error, while parametric models favor high bias. Next, we looked into clustering methods and even coded a KNN class from scratch. Finally, we wrapped up with the pros and cons of non-parametric methods.

In the next chapter, we will get into some more of the advanced topics in supervised machine learning, including recommender systems and neural networks.