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

Resampling imbalanced data

Now that we have an appropriate scoring method to discover exoplanets, it's time to explore strategies such as resampling, undersampling, and oversampling for correcting the imbalanced data causing the low recall score.


One strategy to counteract imbalanced data is to resample the data. It's possible to undersample the data by reducing rows of the majority class and to oversample the data by repeating rows of the minority class.


Our exploration began by selecting 400 rows from 5,087. This is an example of undersampling since the subset contains fewer rows than the original.

Let's write a function that allows us to undersample the data by any number of rows. This function should return the recall score so that we can see how undersampling changes the results. We will begin with the scoring function.

The scoring function

The following function takes XGBClassifier and the number of rows as input and...