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MATLAB for Machine Learning

MATLAB for Machine Learning

By : Giuseppe Ciaburro
4.8 (4)
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MATLAB for Machine Learning

MATLAB for Machine Learning

4.8 (4)
By: Giuseppe Ciaburro

Overview of this book

Discover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications. By navigating the versatile machine learning tools in the MATLAB environment, you’ll learn how to seamlessly interact with the workspace. You’ll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you’ll explore various classification and regression techniques, skillfully applying them with MATLAB functions. This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You’ll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you’ll leverage MATLAB tools for deep learning and managing convolutional neural networks. By the end of the book, you’ll be able to put it all together by applying major machine learning algorithms in real-world scenarios.
Table of Contents (17 chapters)
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1
Part 1: Getting Started with Matlab
4
Part 2: Understanding Machine Learning Algorithms in MATLAB
9
Part 3: Machine Learning in Practice

Evaluating model performance

Model performance refers to how well a model fits the given data and accurately predicts outcomes. It is important to evaluate model performance to assess its reliability and effectiveness in making predictions or in capturing the underlying patterns in the data. One commonly used metric to evaluate model performance is the R-squared value, also known as the coefficient of determination. R-squared measures the proportion of the variance in the dependent variable that can be explained by the independent variables in the model. A higher R-squared value indicates a better fit, as it means a larger proportion of the variability in the data is accounted for by the model.

However, R-squared alone may not provide a complete picture of model performance. Other metrics, such as mean squared error (MSE) or mean absolute error (MAE), can be used to assess the average prediction error of the model. Lower values of MSE or MAE indicate better predictive performance...

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