Summary  
This chapter covers how to implement decision tree models for classification, including selecting feature-based splits using impurity measures (e.g., Gini impurity or entropy) and recursively building a tree structure to predict categorical outcomes.

General domain of usage  
Supervised machine learning classification

Master machine learning techniques using PySpark and real estate data. Learn to build, evaluate, and optimize ML pipelines for classification, regression, and clustering tasks using Spark MLlib.

Comprehensive guide to machine learning with PySpark using real estate data, covering classification, regression, clustering, and pipeline optimization.

Classification: Decision Trees