#### Overview of this book

MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You’ll start by getting your system ready with t he MATLAB environment for machine learning and you’ll see how to easily interact with the Matlab workspace. We’ll then move on to data cleansing, mining and analyzing various data types in machine learning and you’ll see how to display data values on a plot. Next, you’ll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You’ll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you’ll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB.
Title Page
Credits
Foreword
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Getting Started with MATLAB Machine Learning
Importing and Organizing Data in MATLAB
From Data to Knowledge Discovery
Finding Relationships between Variables - Regression Techniques
Pattern Recognition through Classification Algorithms
Identifying Groups of Data Using Clustering Methods
Simulation of Human Thinking - Artificial Neural Networks
Improving the Performance of the Machine Learning Model - Dimensionality Reduction
Machine Learning in Practice

## Feature selection

In general, when we work with high-dimensional datasets, it is a good idea to reduce the number of features to only the most useful ones and discard the rest. This can lead to simpler models that generalize better. Feature selection is the process of reducing inputs for processing and analyzing or identifying the most significant features over the others. This selection of features is necessary to create a functional model so as to achieve a reduction in cardinality, imposing a limit greater than the number of features that must be considered during its creation. In the following figure, a general scheme of a feature selection process is shown:

Figure 8.1: General scheme of a feature selection process

Usually, the data contains redundant information, or more than the necessary information; in other cases, it may contain incorrect information. Feature selection makes the process of creating a model more efficient, for example, decreasing the load on the CPU and the memory...