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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

Leave a review - let other readers know what you think

Predicting heart disease – a case study

You have been asked by a hospital to use machine learning to predict heart disease. Your job is to develop a model and highlight two to three important features that doctors and nurses can focus on to improve patient health.

You decide to use a decision tree classifier with fine-tuned hyperparameters. After the model has been built, you will interpret results using feature_importances_, an attribute that determines the most important features in predicting heart disease.

Heart Disease dataset

The Heart Disease dataset has been uploaded to GitHub as heart_disease.csv. This is a slight modification to the original Heart Disease dataset (https://archive.ics.uci.edu/ml/datasets/Heart+Disease) provided by the UCI Machine Learning Repository (https://archive.ics.uci.edu/ml/index.php) with null values cleaned up for your convenience.

Upload the file and display the first five rows as follows:

df_heart ...

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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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