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MATLAB for Machine Learning

You're reading from  MATLAB for Machine Learning

Product type Book
Published in Aug 2017
Publisher Packt
ISBN-13 9781788398435
Pages 382 pages
Edition 1st Edition
Languages
Authors (2):
Giuseppe Ciaburro Giuseppe Ciaburro
Profile icon Giuseppe Ciaburro
Pavan Kumar Kolluru Pavan Kumar Kolluru
Profile icon Pavan Kumar Kolluru
View More author details
Toc

Table of Contents (17) Chapters close

Title Page
Credits
Foreword
About the Author
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with MATLAB Machine Learning 2. Importing and Organizing Data in MATLAB 3. From Data to Knowledge Discovery 4. Finding Relationships between Variables - Regression Techniques 5. Pattern Recognition through Classification Algorithms 6. Identifying Groups of Data Using Clustering Methods 7. Simulation of Human Thinking - Artificial Neural Networks 8. Improving the Performance of the Machine Learning Model - Dimensionality Reduction 9. Machine Learning in Practice

Predicting a response by decision trees


A decision tree is the graphic demonstration of a choice made or proposed. What seems most interesting is not always useful, and not always are things so clear that you can choose between two solutions immediately. Often, a decision is determined by a series of waterfall conditions. Expressing this concept with tables and numbers is difficult, and even if a table formally represents the phenomenon, it can confuse the reader because the justification of the choice is not immediately apparent.

A tree structure helps us extract the same information with greater readability by putting the right emphasis on the branch we have entered to determine the choice or evaluation. Decision tree technology is useful in identifying a strategy or pursuing a goal by creating a model with probable results. The decision tree graph immediately orients the reading of the result. A plot is much more eloquent than a table full of numbers. The human mind prefers to see the...

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