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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Making a prediction using XGBoost

You have your trained regression model and now need to use it to predict the output values based on the inputs in our test dataset. Follow these steps:

Use the model to make predictions of house value based on the test dataset and put the answers into an array called y_score. Use the predict method and pass it to the X_test dataset:
```
y_score = housevalue_regressor.predict(X_test)
```
You now have a vector, y_score, with the model’s prediction of the housing value based on the inputs in X_test. You’ll use y_score in just a bit to check the accuracy of the model by comparing y_score to the ground truth y_test values.
As before with the iris data in Chapter 2, if you want to predict a housing value based on data that is not in the test or training dataset, to do inference, you just pass the model the equivalent of one row of input data, and it will predict the housing value. Keep in mind that you must maintain the units used in the input...

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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

Making a prediction using XGBoost

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Authors (2)

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