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Hands-On Gradient Boosting with XGBoost and scikit-learn

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

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Product type Paperback
Published in Oct 2020
Publisher Packt
ISBN-13 9781839218354
Length 310 pages
Edition 1st Edition
Languages
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Author (1):
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Corey Wade Corey Wade
Author Profile Icon Corey Wade
Corey Wade
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Table of Contents (15) Chapters Close

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

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):

    df_bikes...
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