Summary

In this chapter, you learnt how to perform classification using some of the most commonly used algorithms. You also understood the advantage and disadvantage of each algorithm. You also learned in depth how a tree-based model works.

You got to grips with why the pre-processing of data using techniques such as standardization is necessary, and implemented various fine-tuning techniques for optimizing a machine learning model. You were able to choose the right performance metrics for your classification problems and explored the concept behind the confusion matrix. You also learned how to compare different models and choose the best performing models.

In the next chapter, you will learn about multi-classification problems and how to tackle imbalanced data.