Part 2: Practical Applications – Data, Features, and Hyperparameters

In this part, you will explore solutions to common questions that arise when modeling data. Through guided activities, you will learn when to use ensemble models or simple classification and regression tree models. You will work with model metrics and apply methods to address common problems with real-life datasets. By applying feature engineering methods, you will improve model performance and handle text and time-based datasets. This part ends with an exploration of interpreting XGBoost models and some practice in extracting feature importance.

This part contains the following chapters:

• Chapter 5, Classification and Regression Trees, Ensembles, and Deep Learning Models – What’s Best for Your Data?
• Chapter 6, Data Cleaning, Imbalanced Data, and Other Data Problems
• Chapter 7, Feature Engineering
• Chapter 8, Encoding Techniques for Categorical Features
• Chapter 9, ...