Effective XGBoost: Optimizing, Tuning by Matt Harrison (.PDF)

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File Size: 28.9 MB

Effective XGBoost: Optimizing, Tuning, Understanding, and Deploying Classification Models (Treading on Python) by Matt Harrison
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Overview: “Effective XGBoost” is the ultimate guide to mastering the art of classification. Whether you’re a seasoned data scientist or just starting out, this comprehensive book will take you from the basics of XGBoost to advanced techniques for optimizing, tuning, understanding, and deploying your models. XGBoost is one of the most popular Machine Learning algorithms used in Data Science today. With its ability to handle large datasets, handle missing values, and deal with non-linear relationships, it has become an essential tool for many data scientists. In this book, you’ll learn everything you need to know to become an expert in XGBoost. XGBoost is both a library and a particular gradient boosted trees (GBT) algorithm. (Although the XGBoost library also supports other – linear – base learners.) GBTs are a class of algorithms that utilize the so-called ensembling – building a very strong ML algorithm by combining many weaker algorithms. Starting with the basics, you’ll learn how to use XGBoost for classification tasks, including how to prepare your data, select the right features, and train your model. From there, you’ll explore advanced techniques for optimizing your models, including hyperparameter tuning, early stopping, and ensemble methods. Machine Learning for tabular data is still a very hands-on artisanal process. A big part of what makes a great tabular data ML model has to do with proper data preparation and feature engineering. This is where Matt’s background with Pandas really comes in handy – many Pandas examples throughout the book are exceptionally valuable in their own right. Chapters end with a great selection of useful exercises.
Genre: Non-Fiction > Tech & Devices

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