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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Using XGBoost to make a prediction

At this point, you have a trained model, ready to be used to classify which type of iris you have based on the measurements of the sepals and petals on a flower. Let’s test out how well it does on your test dataset. To do so, you’ll need to use the predict method and pass it the X_test data:

Make a prediction (classify) based on the test dataset inputs and put the answers into an array called y_score:
```
y_score = iris_classifier.predict(X_test)
```
That’s it – just one line of code to use the model! You can pass any measurement to the model, so long as you provide values for all columns: sepal length, sepal width, petal length, and petal width. Say, for example, you’ve measured an iris and it has a sepal length of 4.5 cm, a sepal width of 3.0 cm, a petal length of 1.5 cm, and a petal width of 0.25 cm. Which type of iris is it?
Next, make a prediction (classify) based on example measurements.
To use the model to...

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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

Using XGBoost to make a prediction

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Authors (2)

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