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

Tuning and scaling XGBClassifier

In this section, we will fine-tune and scale XGBClassifier to obtain the best possible recall_score value for the Exoplanets dataset. First, you will adjust weights using scale_pos_weight, then you will run grid searches to find the best combination of hyperparameters. In addition, you will score models for different subsets of the data before consolidating and analyzing the results.

Adjusting weights

In Chapter 5, XGBoost Unveiled, you used the scale_pos_weight hyperparameter to counteract imbalances in the Higgs boson dataset. Scale_pos_weight is a hyperparameter used to scale the positive weight. The emphasis here on positive is important because XGBoost assumes that a target value of 1 is positive and a target value of 0 is negative.

In the Exoplanet dataset, we have been using the default 1 as negative and 2 as positive as provided by the dataset. We will now switch to 0 as negative and 1 as positive using the .replace() method.

replace...