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_5

5. Nonlinear Modeling With Scikit-Learn, PySpark, and H2O

Tshepo Chris Nokeri¹  

(1)

Pretoria, South Africa

 

This chapter executes and appraises a nonlinear method for binary classification (called logistic regression ) using a diverse set of comprehensive Python frameworks (i.e., Scikit-Learn, Spark MLlib, and H2O). To begin, it clarifies the underlying concept behind the sigmoid function.

Exploring the Logistic Regression Method

The logistic regression method unanimously accepts values and then models them by executing a function (sigmoid) to anticipate values of a categorical ...