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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

Exploring the model’s results

Evaluating results is an essential part of any CNN implementation process. This, of course, is true for any algorithm based on ML. Evaluation metrics are quantitative measures used to assess the performance and quality of a model, algorithm, or system in various tasks, such as ML, data analysis, and optimization. These metrics provide a way to objectively quantify how well a model is performing and to compare different models or approaches.

The type of metric to adopt obviously depends on the type of algorithm we are implementing; in the previous section, we implemented a CNN for the classification of the pistachio species. So, let’s take a look at the metrics available for this type of algorithm.

For a classification task, we can use the following metrics:

  • Accuracy: The proportion of correctly classified instances out of the total instances
  • Precision: The ratio of true positive predictions to the total number of positive...

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