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

Comparing dart

The base learner dart is similar to gbtree in the sense that both are gradient boosted trees. The primary difference is that dart removes trees (called dropout) during each round of boosting.

In this section, we will apply and compare the base learner dart to other base learners in regression and classification problems.

DART with XGBRegressor

Let's see how dart performs on the Diabetes dataset:

  1. First, redefine X and y using load_diabetes as before:

    X, y = load_diabetes(return_X_y=True)
  2. To use dart as the XGBoost base learner, set the XGBRegressor parameter booster='dart' inside the regression_model function:

    regression_model(XGBRegressor(booster='dart', objective='reg:squarederror'))

    The score is as follows:

    65.96444746130739

The dart base learner gives the same result as the gbtree base learner down to two decimal places. The similarity of results is on account of the small dataset and the success of the gbtree...