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

Applying gblinear

It's challenging to find real-world datasets that work best with linear models. It's often the case that real data is messy and more complex models like tree ensembles produce better scores. In other cases, linear models may generalize better.

The success of machine learning algorithms depends on how they perform with real-world data. In the next section, we will apply gblinear to the Diabetes dataset first and then to a linear dataset by construction.

Applying gblinear to the Diabetes dataset

The Diabetes dataset is a regression dataset of 442 diabetes patients provided by scikit-learn. The prediction columns include age, sex, BMI (body mass index), BP (blood pressure), and five serum measurements. The target column is the progression of the disease after 1 year. You can read about the dataset in the original paper here: http://web.stanford.edu/~hastie/Papers/LARS/LeastAngle_2002.pdf.

Scikit-learn's datasets are already split into predictor...