Book Image

Machine Learning with LightGBM and Python

By : Andrich van Wyk
3 (1)
Book Image

Machine Learning with LightGBM and Python

3 (1)
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.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Conventions used
Get in touch
Share Your Thoughts
Download a free PDF copy of this book
1
Part 1: Gradient Boosting and LightGBM Fundamentals
Part 1: Gradient Boosting and LightGBM Fundamentals
Free Chapter
2
Chapter 1: Introducing Machine Learning
Chapter 1: Introducing Machine Learning
Technical requirements
What is machine learning?
Introducing models, datasets, and supervised learning
Decision tree learning
Summary
References
3
Chapter 2: Ensemble Learning – Bagging and Boosting
Chapter 2: Ensemble Learning – Bagging and Boosting
Technical requirements
Ensemble learning
Bagging and random forests
Gradient-boosted decision trees
Advanced boosting algorithm – DART
Summary
References
4
Chapter 3: An Overview of LightGBM in Python
Chapter 3: An Overview of LightGBM in Python
Technical requirements
Introducing LightGBM
Getting started with LightGBM in Python
Building LightGBM models
Summary
References
5
Chapter 4: Comparing LightGBM, XGBoost, and Deep Learning
Chapter 4: Comparing LightGBM, XGBoost, and Deep Learning
Technical requirements
An overview of XGBoost
Deep learning and TabTransformers
Comparing LightGBM, XGBoost, and TabTransformers
Summary
References
6
Part 2: Practical Machine Learning with LightGBM
Part 2: Practical Machine Learning with LightGBM
7
Chapter 5: LightGBM Parameter Optimization with Optuna
Chapter 5: LightGBM Parameter Optimization with Optuna
Technical requirements
Optuna and optimization algorithms
Optimizing LightGBM with Optuna
Summary
References
8
Chapter 6: Solving Real-World Data Science Problems with LightGBM
Chapter 6: Solving Real-World Data Science Problems with LightGBM
Technical requirements
The data science life cycle
Predicting wind turbine power generation with LightGBM
Classifying individual credit scores with LightGBM
Summary
References
9
Chapter 7: AutoML with LightGBM and FLAML
Chapter 7: AutoML with LightGBM and FLAML
Technical requirements
Automated machine learning
Introducing FLAML
Case study – using FLAML with LightGBM
Summary
References
10
Part 3: Production-ready Machine Learning with LightGBM
Part 3: Production-ready Machine Learning with LightGBM
11
Chapter 8: Machine Learning Pipelines and MLOps with LightGBM
Chapter 8: Machine Learning Pipelines and MLOps with LightGBM
Technical requirements
Introducing machine learning pipelines
Understanding MLOps
Deploying an ML pipeline for customer churn
Summary
12
Chapter 9: LightGBM MLOps with AWS SageMaker
Chapter 9: LightGBM MLOps with AWS SageMaker
Technical requirements
An introduction to AWS and SageMaker
Building a LightGBM ML pipeline with Amazon SageMaker
Summary
References
13
Chapter 10: LightGBM Models with PostgresML
Chapter 10: LightGBM Models with PostgresML
Technical requirements
Introducing PostgresML
Getting started with PostgresML
Case study – customer churn with PostgresML
Summary
References
14
Chapter 11: Distributed and GPU-Based Learning with LightGBM
Chapter 11: Distributed and GPU-Based Learning with LightGBM
Technical requirements
Distributed learning with LightGBM and Dask
GPU training for LightGBM
Summary
References
15
Index
Index
Why subscribe?
16
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Summary

In this chapter, we discussed two additional algorithms that may be used to solve tabular learning problems: XGBoost, another gradient-boosting framework, and TabTransformer, a deep learning approach.

We showed how to set up and train both XGBoost models and TabTransformer on two datasets. We also showed how to encode categorical features for tree-based and neural network models. Both datasets also had imbalanced classes, which we had to compensate for during training.

We found that LightGBM and XGBoost produced similarly accurate models but that LightGBM trained models much faster and more efficiently. We also saw the complexity of training DNNs and the lackluster performance on these problems. Deep learning is an extremely powerful technique, but tree-based approaches are often more applicable when working with tabular datasets.

In the next chapter, we focus on more effective parameter optimization with LightGBM using a framework called Optuna.

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