XGBoost is a popular open-source library that provides an efficient, effective, scalable and high-performance implementation of the gradient boosting algorithm. You will be able to build an intuitive and practical understanding of the XGBoost algorithm through our demystifying the complex math underneath and explanation of XGBoost’s benefits over other decision tree ensemble models, including when to use XGBoost or other prediction algorithms. This book provides a hands-on approach to implementation of the XGBoost Python API as well as the scikit-learn API that will help one to be up-and-running and productive in no time. with step-by-step explanations of essential concepts, as well as practical examples, this book begins with a brief introduction to machine learning concepts, then dives into the fundamentals of XGBoost and its benefits before exploring practical applications. You will get hands-on experience using XGBoost through practical use cases on classification, regression, and time-series data. By the end of this book, you will have an understanding of the XGBoost algorithm, have installed the XGBoost API, downloaded and prepared a practical dataset, trained the XGBoost model, make predictions, and evaluated and deployed models using the Python and scikit-learn API.

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Spis treści

XGBoost for Regression Predictive Modeling and Time Series Analysis. Learn how to build, evaluate, and deploy predictive models with expert guidance eBook -- spis treści

• 1. An Overview of Machine Learning, Classification, and Regression
• 2. XGBoost Quick Start Guide with an Iris Data Case Study
• 3. Demystifying the XGBoost Paper
• 4. Adding On to the Quick Start – Switching Out the Dataset with a Housing Data Case Study
• 5. Classification and Regression Trees, Ensembles, and Deep Learning Models – What's Best for Your Data?
• 6. Data Cleaning, Imbalanced Data, and Other Data Problems
• 7. Feature Engineering
• 8. Encoding Techniques for Categorical Features
• 9. Using XGBoost for Time Series Forecasting
• 10. Model Interpretability, Explainability, and Feature Importance with XGBoost
• 11. Metrics for Model Evaluations and Comparisons
• 12. Managing a Feature Engineering Pipeline in Training and Inference
• 13. Deploying Your XGBoost Model