Hands-On Gradient Boosting with XGBoost and scikit-learn

Perform accessible machine learning and extreme gradient boosting with Python

Corey Wade 지음

Packt(GCO Science) 2020년 10월 16일 출간

작품소개

책소개

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

▶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

▶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

▶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.

목차

▶TABLE of CONTENTS
▷Section 1: Bagging and Boosting
-Chapter 1: Machine Learning Landscape
-Chapter 2: Decision Trees in Depth
-Chapter 3: Bagging with Random Forests
-Chapter 4: From Gradient Boosting to XGBoost

▷Section 2: XGBoost
-Chapter 5: XGBoost Unveiled
-Chapter 6: XGBoost Hyperparameters
-Chapter 7: Discovering Exoplanets with XGBoost

▷Section 3: Advanced XGBoost
-Chapter 8: XGBoost Alternative Base Learners
-Chapter 9: XGBoost Kaggle Masters
-Chapter 10: XGBoost Model Deployment

책 속으로

▶What this book covers
- Chapter 1, Machine Learning Landscape, presents XGBoost within the general context of machine learning by introducing linear regression and logistic regression before comparing results with XGBoost. pandas is introduced to preprocess raw data for machine learning by converting categorical columns and clearing null values in a variety of ways.

- Chapter 2, Decision Trees in Depth, presents a detailed examination of decision tree hyperparameters that are used by XGBoost, along with a graphical and statistical analysis of variance and bias that highlights the importance of overfitting, a theme touched on throughout the book.

- Chapter 3, Bagging with Random Forests, presents a general survey of random forests as an XGBoost competitor with a focus on bagging. Additional XGBoost hyperparameters shared with random forests such as n_esimtators and subsample are thoroughly covered.

- Chapter 4, From Gradient Boosting to XGBoost, covers boosting fundamentals, building a booster from scratch in scikit-learn, fine-tuning new XGBoost hyperparameters such as eta, and comparing runtimes between gradient boosting and XGBoost to highlight XGBoost's impressive speed.

- Chapter 5, XGBoost Unveiled, analyzes the mathematical derivations of XGBoost algorithms and features a historically relevant case study featuring XGBoost's role as the winning model in the Higgs Boson Kaggle Competition. Standard XGBoost parameters are discussed, base models are built, and the original Python API is covered.

- Chapter 6, XGBoost Hyperparameters, covers all essential XGBoost hyperparameters, summarizes previous tree ensemble hyperparameters, and uses original grid search functions to fine-tune XGBoost models to optimize scores.

- Chapter 7, Discovering Exoplanets with XGBoost, gives you the opportunity to discover exoplanets with XGBoost in a top-to-bottom case study. The pitfalls of imbalanced datasets are analyzed with the confusion matrix and classification report lea

출판사 서평

▶ Preface
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.

작가정보

저자(글) Corey Wade

Corey Wade, M.S. Mathematics, M.F.A. Writing and 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 the Independent Study Program of Berkeley High School, where he teaches programming and advanced math. His additional experience includes teaching natural language processing with Hello World, developing data science curricula with Pathstream, and publishing original statistics (3NG) and machine learning articles with Towards Data Science, Springboard, and Medium. Corey is co-author of the Python Workshop, also published by Packt.