#### 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

## Predicting a response by decision trees

A decision tree is the graphic demonstration of a choice made or proposed. What seems most interesting is not always useful, and not always are things so clear that you can choose between two solutions immediately. Often, a decision is determined by a series of waterfall conditions. Expressing this concept with tables and numbers is difficult, and even if a table formally represents the phenomenon, it can confuse the reader because the justification of the choice is not immediately apparent.

A tree structure helps us extract the same information with greater readability by putting the right emphasis on the branch we have entered to determine the choice or evaluation. Decision tree technology is useful in identifying a strategy or pursuing a goal by creating a model with probable results. The decision tree graph immediately orients the reading of the result. A plot is much more eloquent than a table full of numbers. The human mind prefers to see the...