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

Tuning decision tree hyperparameters

Hyperparameters are not the same as parameters.

In machine learning, parameters are adjusted when the model is being tuned. The weights in linear and Logistic Regression, for example, are parameters adjusted during the build phase to minimize errors. Hyperparameters, by contrast, are chosen in advance of the build phase. If no hyperparameters are selected, default values are used.

Decision Tree regressor

The best way to learn about hyperparameters is through experimentation. Although there are theories behind the range of hyperparameters chosen, results trump theory. Different datasets see improvements with different hyperparameter values.

Before selecting hyperparameters, let's start by finding a baseline score using a DecisionTreeRegressor and cross_val_score with the following steps:

  1. Download the 'bike_rentals_cleaned' dataset and split it into X_bikes (predictor columns) and y_bikes (training columns):