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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

Product type Book

Published in Oct 2020

Publisher Packt

ISBN-13 9781839218354

Pages 310 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Corey Wade

Table of Contents (15) Chapters

Preface

1. Section 1: Bagging and Boosting

2. Chapter 1: Machine Learning Landscape

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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Pushing random forest boundaries – case study

Imagine you work for a bike rental company and your goal is to predict the number of bike rentals per day depending upon the weather, the time of day, the time of year, and the growth of the company.

Earlier in this chapter, you implemented a random forest regressor with cross-validation to obtain an RMSE of 945 bikes. Your goal is to modify the random forest to obtain the lowest error score possible.

Preparing the dataset

Earlier in this chapter, you downloaded the dataset df_bikes and split it into X_bikes and y_bikes. Now that you are doing some serious testing, you decide to split X_bikes and y_bikes into training sets and test sets as follows:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_bikes, y_bikes, random_state=2)

n_estimators

Start by choosing a reasonable value for n_estimators. Recall that n_estimators can be increased to improve accuracy...

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Authors (1)

Corey Wade

Corey Wade, M.S. Mathematics, M.F.A. Writing & Consciousness, is the founder and director of Berkeley Coding Academy where he teaches Machine Learning and AI to teens from all over the world. Additionally, Corey chairs the Math Department at Berkeley Independent Study where he has received multiple grants to run after-school coding programs to help bridge the tech skills gap. Additional experiences include teaching Natural Language Processing with Hello World, developing Data Science curricula with Pathstream, and publishing statistics and machine learning models with Towards Data Science, Springboard, and Medium.

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