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

View More author details

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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Comparing models with the housing dataset

Let’s start by loading and preparing the data for modeling:

Set up the Python environment: Start by making a copy of the code from Chapter 4 and modifying it. Like in Chapter 4, we’ll need the pandas and NumPy libraries. We’ve chosen to name the file housingvaluemodelcomparison.ipynb:

# ----------------------------------------
# filename housingvaluemodelcomparison.ipynb
# purpose compare predictions of house value
# by different models
# author Joyce Weiner
# revision 1.0
# revision history 1.0 - initial script
# ----------------------------------------
import pandas as pd
import numpy as np

Load the California housing dataset from scikit-learn: The housing dataset is built into scikit-learn. The fetch_california_housing function has two useful parameters that allow you to load the data as a pandas DataFrame and put the features (X values) and the target y value into separate variables in just one line of code...