Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2022

T. C. NokeriData Science Solutions with Pythonhttps://doi.org/10.1007/978-1-4842-7762-1_6

6. Tree Modeling and Gradient Boosting with Scikit-Learn, XGBoost, PySpark, and H2O

Tshepo Chris Nokeri¹  

(1)

Pretoria, South Africa

 

This chapter executes and appraises a tree-based method (the decision tree method) and an ensemble method (the gradient boosting trees method) using a diverse set of comprehensive Python frameworks (i.e., Scikit-Learn, XGBoost, PySpark, and H2O). To begin, the chapter clarifies how decision trees compute the probabilities of classes.

Decision Trees

The decision tree is an elementary, non-parametric method suitable for linear and nonlinear ...