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Machine Learning Algorithms - Second Edition

You're reading from Machine Learning Algorithms - Second Edition

Product type Book

Published in Aug 2018

Publisher Packt

ISBN-13 9781789347999

Pages 522 pages

Edition 2nd Edition

Languages

Concepts

Machine Learning

Table of Contents (19) Chapters

Preface

1. A Gentle Introduction to Machine Learning

2. Important Elements in Machine Learning

3. Feature Selection and Feature Engineering

4. Regression Algorithms

5. Linear Classification Algorithms

6. Naive Bayes and Discriminant Analysis

7. Support Vector Machines

8. Decision Trees and Ensemble Learning

9. Clustering Fundamentals

10. Advanced Clustering

11. Hierarchical Clustering

12. Introducing Recommendation Systems

13. Introducing Natural Language Processing

14. Topic Modeling and Sentiment Analysis in NLP

15. Introducing Neural Networks

16. Advanced Deep Learning Models

17. Creating a Machine Learning Architecture

18. Other Books You May Enjoy

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Evaluation methods based on the ground truth

In this section, we will present some evaluation methods that require knowledge of the ground truth. This condition is not always easy to obtain because clustering is normally applied as an unsupervised method; however, in some cases, the training set has been manually (or automatically) labeled, and it's useful to evaluate a model before predicting the clusters of new samples.

Homogeneity

An important requirement for a clustering algorithm (given the ground truth) is that each cluster should only contain samples belonging to a single class. In Chapter 2, Important Elements in Machine Learning, we have defined the concepts of entropy H(X) and conditional entropy H(X|Y), which...

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