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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
Author (1):
Corey Wade Corey Wade
Profile icon Corey Wade
Toc

Table of Contents (15) Chapters close

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

Contrasting variance and bias

Imagine that you have the data points displayed in the following graph. Your task is to fit a line or curve that will allow you to make predictions for new points.

Here is a graph of random points:

Figure 2.3 – Graph of random points

Figure 2.3 – Graph of random points

One idea is to use Linear Regression, which minimizes the square of the distance between each point and the line, as shown in the following graph:

Figure 2.4 – Minimizing distance using Linear Regression

Figure 2.4 – Minimizing distance using Linear Regression

A straight line generally has high bias. In machine learning bias is a mathematical term that comes from estimating the error when applying the model to a real-life problem. The bias of the straight line is high because the predictions are restricted to the line and fail to account for changes in the data.

In many cases, a straight line is not complex enough to make accurate predictions. When this happens, we say that the machine learning model...

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