You're reading from XGBoost for Regression Predictive Modeling and Time Series Analysis Learn how to build, evaluate, and deploy predictive models with expert guidance

Product type Paperback

Published in Dec 2024

Publisher Packt

ISBN-13 9781805123057

Length 308 pages

Edition 1st Edition

Concepts

Data Governance

Authors (2):

Joyce Weiner

Partha Pritam Deka

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Table of Contents (19) Chapters

Preface

1. Part 1:Introduction to Machine Learning and XGBoost with Case Studies

2. Chapter 1: An Overview of Machine Learning, Classification, and Regression FREE CHAPTER

3. Chapter 2: XGBoost Quick Start Guide with an Iris Data Case Study

4. Chapter 3: Demystifying the XGBoost Paper

5. Chapter 4: Adding on to the Quick Start – Switching out the Dataset with a Housing Data Case Study

6. Part 2: Practical Applications – Data, Features, and Hyperparameters

7. Chapter 5: Classification and Regression Trees, Ensembles, and Deep Learning Models – What’s Best for Your Data?

8. Chapter 6: Data Cleaning, Imbalanced Data, and Other Data Problems

9. Chapter 7: Feature Engineering

10. Chapter 8: Encoding Techniques for Categorical Features

11. Chapter 9: Using XGBoost for Time Series Forecasting

12. Chapter 10: Model Interpretability, Explainability, and Feature Importance with XGBoost

13. Part 3: Model Evaluation Metrics and Putting Your Model into Production

14. Chapter 11: Metrics for Model Evaluations and Comparisons

15. Chapter 12: Managing a Feature Engineering Pipeline in Training and Inference

16. Chapter 13: Deploying Your XGBoost Model

17. Index

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18. Other Books You May Enjoy

Comparing XGBoost to CART

So far, you’ve compared XGBoost to linear fit. For the housing data, you’ve seen that XGBoost provides an improvement with better RMSE and R2 values, even though it takes longer to execute than linear regression. In the following steps, we will compare it to CART in terms of fit and performance, as implemented by scikit-learn:

1. Fit a regression tree model using CART: To fit a CART model, use the DecisionTree module in scikit-learn, specifically sklearn.tree.DecisionTreeRegressor since you will be performing regression to predict housing values. First, set up the model by calling DecisionTreeRegressor and put the result in housing_CART. Use the default settings so that no values are passed. This is where you can set the model hyperparameters. Next, fit the model by applying the .fit method:

%%time
from sklearn.tree import DecisionTreeRegressor
housing_CART = DecisionTreeRegressor()
housing_CART_regression = housing_CART.fit(X_train, y_train...

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You're reading from XGBoost for Regression Predictive Modeling and Time Series Analysis Learn how to build, evaluate, and deploy predictive models with expert guidance

Table of Contents (19) Chapters

Comparing XGBoost to CART

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Authors (2)

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