You're reading from Hands-On Gradient Boosting with XGBoost and scikit-learn Perform accessible machine learning and extreme gradient boosting with Python

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781839218354

Length 310 pages

Edition 1st Edition

Languages

Python

Tools

Scikit-learn

Concepts

Machine Learning

Author (1):

Wade

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Table of Contents (15) Chapters

Preface

1. Section 1: Bagging and Boosting

2. Chapter 1: Machine Learning Landscape FREE CHAPTER

3. Chapter 2: Decision Trees in Depth

4. Chapter 3: Bagging with Random Forests

5. Chapter 4: From Gradient Boosting to XGBoost

6. Section 2: XGBoost

7. Chapter 5: XGBoost Unveiled

8. Chapter 6: XGBoost Hyperparameters

9. Chapter 7: Discovering Exoplanets with XGBoost

10. Section 3: Advanced XGBoost

11. Chapter 8: XGBoost Alternative Base Learners

12. Chapter 9: XGBoost Kaggle Masters

13. Chapter 10: XGBoost Model Deployment

14. Other Books You May Enjoy

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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:

First, redefine X and y using load_diabetes as before:
```
X, y = load_diabetes(return_X_y=True)
```
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...