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MATLAB for Machine Learning

You're reading from   MATLAB for Machine Learning Unlock the power of deep learning for swift and enhanced results

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Product type Paperback
Published in Jan 2024
Publisher Packt
ISBN-13 9781835087695
Length 374 pages
Edition 2nd Edition
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Author (1):
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Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Table of Contents (17) Chapters Close

Preface 1. Part 1: Getting Started with Matlab
2. Chapter 1: Exploring MATLAB for Machine Learning FREE CHAPTER 3. Chapter 2: Working with Data in MATLAB 4. Part 2: Understanding Machine Learning Algorithms in MATLAB
5. Chapter 3: Prediction Using Classification and Regression 6. Chapter 4: Clustering Analysis and Dimensionality Reduction 7. Chapter 5: Introducing Artificial Neural Network Modeling 8. Chapter 6: Deep Learning and Convolutional Neural Networks 9. Part 3: Machine Learning in Practice
10. Chapter 7: Natural Language Processing Using MATLAB 11. Chapter 8: MATLAB for Image Processing and Computer Vision 12. Chapter 9: Time Series Analysis and Forecasting with MATLAB 13. Chapter 10: MATLAB Tools for Recommender Systems 14. Chapter 11: Anomaly Detection in MATLAB 15. Index 16. Other Books You May Enjoy

Feature selection and feature extraction using MATLAB

In MATLAB, there are several built-in functions and toolboxes that can be used for dimensionality reduction. In the next section, we will explore some practical examples of the dimensionality reduction algorithm in the MATLAB environment.

Stepwise regression for feature selection

Regression analysis is a valuable approach for understanding the impact of independent variables on a dependent variable. It allows us to identify predictors that hold greater influence over the model’s response. Stepwise regression is a variable selection method used to choose a subset of predictors that exhibit the strongest relationship with the dependent variable. There are three common variable selection algorithms:

  • Forward method: The forward method starts with an empty model, where no predictors are initially selected. In the first step, the variable showing the most significant association at a statistical level is added. In...
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