Machine Learning with scikit-learn Quick Start Guide

Summary

This chapter introduced you to two fundamental supervised machine learning algorithms: the Naive Bayes algorithm and linear support vector machines. More specifically, you learned about the following topics:

• How the Bayes theorem is used to produce a probability, to indicate whether a data point belongs to a particular class or category
• Implementing the Naive Bayes classifier in scikit-learn
• How the linear support vector machines work under the hood
• Implementing the linear support vector machines in scikit-learn
• Optimizing the inverse regularization strength, both graphically and by using the GridSearchCV algorithm
• How to scale your data for a potential improvement in performance

In the next chapter, you will learn about the other ...