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Machine Learning with LightGBM and Python

You're reading from   Machine Learning with LightGBM and Python A practitioner's guide to developing production-ready machine learning systems

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781800564749
Length 252 pages
Edition 1st Edition
Languages
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Author (1):
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Andrich van Wyk Andrich van Wyk
Author Profile Icon Andrich van Wyk
Andrich van Wyk
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Gradient Boosting and LightGBM Fundamentals
2. Chapter 1: Introducing Machine Learning FREE CHAPTER 3. Chapter 2: Ensemble Learning – Bagging and Boosting 4. Chapter 3: An Overview of LightGBM in Python 5. Chapter 4: Comparing LightGBM, XGBoost, and Deep Learning 6. Part 2: Practical Machine Learning with LightGBM
7. Chapter 5: LightGBM Parameter Optimization with Optuna 8. Chapter 6: Solving Real-World Data Science Problems with LightGBM 9. Chapter 7: AutoML with LightGBM and FLAML 10. Part 3: Production-ready Machine Learning with LightGBM
11. Chapter 8: Machine Learning Pipelines and MLOps with LightGBM 12. Chapter 9: LightGBM MLOps with AWS SageMaker 13. Chapter 10: LightGBM Models with PostgresML 14. Chapter 11: Distributed and GPU-Based Learning with LightGBM 15. Index 16. Other Books You May Enjoy

Solving Real-World Data Science Problems with LightGBM

With the preceding chapters, we have slowly been building out a toolset for us to be able to solve machine learning problems. We’ve seen examples of examining our data, addressing data issues, and creating models. This chapter formally defines and applies the data science process to two case studies.

The chapter gives a detailed overview of the data science life cycle and all the steps it encompasses. The concepts of problem definition, data exploration, data cleaning, modeling, and reporting are discussed in a regression and classification problem context. We also look at preparing data for modeling and building optimized LightGBM models using our learned techniques. Finally, we look deeper at utilizing a trained model as an introduction to machine learning operations (MLOps).

The main topics of this chapter are as follows:

  • The data science life cycle
  • Predicting wind turbine power generation with LightGBM...
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