Book Image

Hands-On Gradient Boosting with XGBoost and scikit-learn

By : Corey Wade
Book Image

Hands-On Gradient Boosting with XGBoost and scikit-learn

By: Corey Wade

Overview of this book

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.
Table of Contents (15 chapters)
Section 1: Bagging and Boosting
Section 2: XGBoost
Section 3: Advanced XGBoost

Preparing data and base models

Before introducing and applying XGBoost hyperparameters, let's prepare by doing the following:

  • Getting the heart disease dataset

  • Building an XGBClassifier model

  • Implementing StratifiedKFold

  • Scoring a baseline XGBoost model

  • Combining GridSearchCV with RandomizedSearchCV to form one powerful function

Good preparation is essential for gaining accuracy, consistency, and speed when fine-tuning hyperparameters.

The heart disease dataset

The dataset used throughout this chapter is the heart disease dataset originally presented in Chapter 2, Decision Trees in Depth. We have chosen the same dataset to maximize the time spent doing hyperparameter fine-tuning, and to minimize the time spent on data analysis. Let's begin the process:

  1. Go to to load heart_disease.csv into a DataFrame and display the...