You're reading from Python Machine Learning by Example Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn

Product type Paperback

Published in Oct 2020

Publisher Packt

ISBN-13 9781800209718

Length 526 pages

Edition 3rd Edition

Languages

Python

Tools

PyTorch

Concepts

Machine Learning

Author (1):

Yuxi (Hayden) Liu

View More author details

Table of Contents (17) Chapters

Preface

1. Getting Started with Machine Learning and Python

2. Building a Movie Recommendation Engine with Naïve Bayes FREE CHAPTER

3. Recognizing Faces with Support Vector Machine

4. Predicting Online Ad Click-Through with Tree-Based Algorithms

5. Predicting Online Ad Click-Through with Logistic Regression

6. Scaling Up Prediction to Terabyte Click Logs

7. Predicting Stock Prices with Regression Algorithms

8. Predicting Stock Prices with Artificial Neural Networks

9. Mining the 20 Newsgroups Dataset with Text Analysis Techniques

10. Discovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic Modeling

11. Machine Learning Best Practices

12. Categorizing Images of Clothing with Convolutional Neural Networks

13. Making Predictions with Sequences Using Recurrent Neural Networks

14. Making Decisions in Complex Environments with Reinforcement Learning

15. Other Books You May Enjoy

16. Index

Exploring a decision tree from the root to the leaves

A decision tree is a tree-like graph, that is, a sequential diagram illustrating all of the possible decision alternatives and their corresponding outcomes. Starting from the root of a tree, every internal node represents the basis on which a decision is made. Each branch of a node represents how a choice may lead to the next nodes. And, finally, each terminal node, the leaf, represents the outcome produced.

For example, we have just made a couple of decisions that brought us to the point of using a decision tree to solve our advertising problem:

Figure 4.2: Using a decision tree to find the right algorithm

The first condition, or the root, is whether the feature type is numerical or categorical. Ad clickstream data contains mostly categorical features, so it goes to the right branch. In the next node, our work needs to be interpretable by non-technical clients. So, it goes...