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Machine Learning with LightGBM and Python

Machine Learning with LightGBM and Python

By : Andrich van Wyk
4.4 (8)
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Machine Learning with LightGBM and Python

Machine Learning with LightGBM and Python

4.4 (8)
By: Andrich van Wyk

Overview of this book

Machine Learning with LightGBM and Python is a comprehensive guide to learning the basics of machine learning and progressing to building scalable machine learning systems that are ready for release. This book will get you acquainted with the high-performance gradient-boosting LightGBM framework and show you how it can be used to solve various machine-learning problems to produce highly accurate, robust, and predictive solutions. Starting with simple machine learning models in scikit-learn, you’ll explore the intricacies of gradient boosting machines and LightGBM. You’ll be guided through various case studies to better understand the data science processes and learn how to practically apply your skills to real-world problems. As you progress, you’ll elevate your software engineering skills by learning how to build and integrate scalable machine-learning pipelines to process data, train models, and deploy them to serve secure APIs using Python tools such as FastAPI. By the end of this book, you’ll be well equipped to use various -of-the-art tools that will help you build production-ready systems, including FLAML for AutoML, PostgresML for operating ML pipelines using Postgres, high-performance distributed training and serving via Dask, and creating and running models in the Cloud with AWS Sagemaker.
Table of Contents (17 chapters)
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1
Part 1: Gradient Boosting and LightGBM Fundamentals
6
Part 2: Practical Machine Learning with LightGBM
10
Part 3: Production-ready Machine Learning with LightGBM

Gradient-boosted decision trees

Gradient boosting is an ensemble learning methodology that combines multiple models sequentially to produce a more robust ensemble model. Unlike bagging, where multiple strong models are used (in parallel), with boosting, multiple weak learners are trained, each learning from the mistakes of those before it to build a more accurate and robust ensemble model. Another distinct difference from bagging is that each model uses the entire dataset for training.

Note

As discussed next, gradient boosting always builds a series of regression trees to form part of the ensemble, regardless of whether a regression or classification problem is solved. Gradient boosting is also called Multiple Additive Regression Trees (MART).

Abstractly, the boosting process starts with a weak base learner. In the case of decision trees, the base learner might have only a single split (also known as a decision stump). The error residuals (the difference between the predicted...

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