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 10: XGBoost Model Deployment

In this final chapter on XGBoost, you will put everything together and develop new techniques to build a robust machine learning model that is industry ready. Deploying models for industry is a little different than building models for research and competitions. In industry, automation is important since new data arrives frequently. More emphasis is placed on procedure, and less emphasis is placed on gaining minute percentage points by tweaking machine learning models.

Specifically, in this chapter, you will gain significant experience with one-hot encoding and sparse matrices. In addition, you will implement and customize scikit-learn transformers to automate a machine learning pipeline to make predictions on data that is mixed with categorical and numerical columns. At the end of this chapter, your machine learning pipeline will be ready for any incoming data.

In this chapter, we cover the following topics:

  • Encoding mixed data

  • ...