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)
1
Section 1: Bagging and Boosting
6
Section 2: XGBoost
10
Section 3: Advanced XGBoost

Chapter 9: XGBoost Kaggle Masters

In this chapter, you will learn valuable tips and tricks from Kaggle Masters who used XGBoost to win Kaggle competitions. Although we will not enter a Kaggle competition here, the skills that you will gain can apply to building stronger machine learning models in general. Specifically, you will learn why an extra hold-out set is critical, how to feature engineer new columns of data with mean encoding, how to implement VotingClassifier and VotingRegressor to build non-correlated machine learning ensembles, and the advantages of stacking a final model.

In this chapter, we will cover the following main topics:

  • Exploring Kaggle competitions

  • Engineering new columns of data

  • Building non-correlated ensembles

  • Stacking final models