Distributed Machine Learning with PySpark: Migrating Effortlessly from Pandas and Scikit-Learn

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2023

A. TestasDistributed Machine Learning with PySparkhttps://doi.org/10.1007/978-1-4842-9751-3_8

8. Decision Tree Classification with Pandas, Scikit-Learn, and PySpark

Abdelaziz Testas¹  

(1)

Fremont, CA, USA

 

In this chapter, we will continue with classification as a form of supervised learning. Our objective is to develop, train, and evaluate a decision tree classification model for predicting the species of an Iris flower based on its feature measurements. We will leverage the well-known Iris dataset, which consists of measurements of four features (sepal length, sepal width, petal length, and petal width) from three distinct species of Iris flowers (setosa, ...