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

Searching for exoplanets

In this section, we'll begin the search for exoplanets by analyzing the Exoplanets dataset. We'll provide historical context for the discovery of exoplanets before attempting to detect them via plotting and observing light graphs. Plotting time series is a valuable machine learning skill that may be used to gain insights into any time series datasets. Finally, we'll make initial predictions using machine learning before revealing a glaring shortcoming.

Historical background

Astronomers have been gathering information from light since antiquity. With the advent of the telescope, astronomical knowledge surged in the 17th century. The combination of telescopes and mathematical models empowered 18th-century astronomers to predict planetary locations and eclipses within our own solar system with great precision.

In the 20th century, astronomical research continued with more advanced technology and more complex mathematics. Planets revolving...