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Machine Learning Algorithms

You're reading from Machine Learning Algorithms

Product type Book

Published in Jul 2017

Publisher Packt

ISBN-13 9781785889622

Pages 360 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Table of Contents (22) Chapters

Title Page

Credits

About the Author

About the Reviewers

www.PacktPub.com

Customer Feedback

Preface

1. A Gentle Introduction to Machine Learning

2. Important Elements in Machine Learning

3. Feature Selection and Feature Engineering

4. Linear Regression

5. Logistic Regression

6. Naive Bayes

7. Support Vector Machines

8. Decision Trees and Ensemble Learning

9. Clustering Fundamentals

10. Hierarchical Clustering

11. Introduction to Recommendation Systems

12. Introduction to Natural Language Processing

13. Topic Modeling and Sentiment Analysis in NLP

14. A Brief Introduction to Deep Learning and TensorFlow

15. Creating a Machine Learning Architecture

Evaluation methods based on the ground truth

In this section, we present some evaluation methods that require the 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 contain only 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 measures the uncertainty of X given the knowledge of Y. Therefore, if the class set is denoted as C and the cluster set as K, H(C|K) is a measure of the uncertainty in determining the right class after having clustered the dataset. To have a homogeneity score, it's necessary...

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