Summary

In this chapter, you learned how to perform classification using some of the most commonly used algorithms. After discovering how tree-based models work, you were able to calculate information gain, Gini values, and entropy. You applied these concepts to train decision tree and random forest models on two datasets.

Later in the chapter, you explored why the preprocessing of data using techniques such as standardization is necessary. You implemented various fine-tuning techniques for optimizing a machine learning model. Next, you identified the right performance metrics for your classification problems and visualized performance summaries using a confusion matrix. You also explored other evaluation metrics including precision, recall, F1 score, ROC curve, and the area under the curve.

You implemented these techniques on case studies such as the telecom dataset and customer churn prediction and discovered how similar approaches can be followed in predicting whether a customer...