Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python

Wade, Corey

ISBN 10: 1839218355 / ISBN 13: 9781839218354

Verlag: Packt Publishing, 2020

Beschreibung

Bestandsnummer des Verkäufers 4CJH0200BPVL

Bibliografische Details

• Titel: Hands-On Gradient Boosting with XGBoost and scikit-learn: Perform accessible machine learning and extreme gradient boosting with Python
• Verlag: Packt Publishing
• Erscheinungsjahr: 2020
• Sprache: Englisch
• ISBN-10: 1839218355
• ISBN-13: 9781839218354
• Einband: Softcover
• Zustand: good

Über diesen Titel

Inhaltsangabe

Get to grips with building robust XGBoost models using Python and scikit-learn for deployment

Key Features

• Get up and running with machine learning and understand how to boost models with XGBoost in no time
• Build real-world machine learning pipelines and fine-tune hyperparameters to achieve optimal results
• Discover tips and tricks and gain innovative insights from XGBoost Kaggle winners

Book Description

XGBoost is an industry-proven, open-source software library that provides a gradient boosting framework for scaling billions of data points quickly and efficiently.

The book introduces machine learning and XGBoost in scikit-learn before building up to the theory behind gradient boosting. You'll cover decision trees and analyze bagging in the machine learning context, learning hyperparameters that extend to XGBoost along the way. You'll build gradient boosting models from scratch and extend gradient boosting to big data while recognizing speed limitations using timers. Details in XGBoost are explored with a focus on speed enhancements and deriving parameters mathematically. With the help of detailed case studies, you'll practice building and fine-tuning XGBoost classifiers and regressors using scikit-learn and the original Python API. You'll leverage XGBoost hyperparameters to improve scores, correct missing values, scale imbalanced datasets, and fine-tune alternative base learners. Finally, you'll apply advanced XGBoost techniques like building non-correlated ensembles, stacking models, and preparing models for industry deployment using sparse matrices, customized transformers, and pipelines.

By the end of the book, you'll be able to build high-performing machine learning models using XGBoost with minimal errors and maximum speed.

What you will learn

• Build gradient boosting models from scratch
• Develop XGBoost regressors and classifiers with accuracy and speed
• Analyze variance and bias in terms of fine-tuning XGBoost hyperparameters
• Automatically correct missing values and scale imbalanced data
• Apply alternative base learners like dart, linear models, and XGBoost random forests
• Customize transformers and pipelines to deploy XGBoost models
• Build non-correlated ensembles and stack XGBoost models to increase accuracy

Who this book is for

This book is for data science professionals and enthusiasts, data analysts, and developers who want to build fast and accurate machine learning models that scale with big data. Proficiency in Python, along with a basic understanding of linear algebra, will help you to get the most out of this book.

Table of Contents

1. Machine Learning Landscape
2. Decision Trees in Depth
3. Bagging with Random Forests
4. From Gradient Boosting to XGBoost
5. XGBoost Unveiled
6. XGBoost Hyperparameters
7. Discovering Exoplanets with XGBoost
8. XGBoost Alternative Base Learners
9. XGBoost Kaggle Masters
10. XGBoost Model Deployment

Über die Autorin bzw. den Autor

Corey Wade, M.S. Mathematics, M.F.A. Writing & Consciousness, is the founder and director of Berkeley Coding Academy where he teaches Machine Learning and AI to teens from all over the world. Additionally, Corey chairs the Math Department at Berkeley Independent Study where he has received multiple grants to run after-school coding programs to help bridge the tech skills gap. Additional experiences include teaching Natural Language Processing with Hello World, developing Data Science curricula with Pathstream, and publishing statistics and machine learning models with Towards Data Science, Springboard, and Medium.