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MATLAB for Machine Learning

You're reading from   MATLAB for Machine Learning Practical examples of regression, clustering and neural networks

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781788398435
Length 382 pages
Edition 1st Edition
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Authors (2):
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Pavan Kumar Kolluru Pavan Kumar Kolluru
Author Profile Icon Pavan Kumar Kolluru
Pavan Kumar Kolluru
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (10) Chapters Close

Preface 1. Getting Started with MATLAB Machine Learning FREE CHAPTER 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

How to create a linear regression model

More generally, to create a linear regression model, use the fitlm() function. This function creates a LinearModel object. The object in the workspace has a series of properties that can be immediately viewed by simply clicking on it. Methods such as plot, plotResiduals, and plotDiagnostics are available if you want to create plots and perform a diagnostic analysis.

LinearModel is an object comprising training data, model description, diagnostic information, and fitted coefficients for a linear regression.

By default, fitlm() takes the last variable in the table or dataset array as the response. Otherwise, we have to specify predictors and response variables, for example, as a formula. In addition, we can set a specific column as the response variable by using the ResponseVar name-value pair argument. To use a set of the columns as predictors...

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