Summary  
This chapter explains how to implement code that evaluates predictive models by computing performance metrics and comparing their results to select the best-performing model.

General domain of usage  
Machine learning

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.

Evaluating and Comparing Models