Book Image

Designing Machine Learning Systems with Python

By : David Julian
Book Image

Designing Machine Learning Systems with Python

By: David Julian

Overview of this book

Machine learning is one of the fastest growing trends in modern computing. It has applications in a wide range of fields, including economics, the natural sciences, web development, and business modeling. In order to harness the power of these systems, it is essential that the practitioner develops a solid understanding of the underlying design principles. There are many reasons why machine learning models may not give accurate results. By looking at these systems from a design perspective, we gain a deeper understanding of the underlying algorithms and the optimisational methods that are available. This book will give you a solid foundation in the machine learning design process, and enable you to build customised machine learning models to solve unique problems. You may already know about, or have worked with, some of the off-the-shelf machine learning models for solving common problems such as spam detection or movie classification, but to begin solving more complex problems, it is important to adapt these models to your own specific needs. This book will give you this understanding and more.
Table of Contents (16 chapters)
Designing Machine Learning Systems with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
1
Thinking in Machine Learning
Index

Multiclass classification


So far, we have just looked at binary classification. For multiclass classification, we assume that each instance belongs to only one class. A slightly different classification problem is where each sample can belong to more than one target class. This is called multi-label classification. We can employ similar strategies on each of these types of problem.

There are two basic approaches:

  • One versus all

  • One versus many

In the one versus all approach, a single multiclass problem is transformed into a number of binary classification problems. This is called the one versus all technique because we take each class in turn and fit a hypothesis function for that particular class, assigning a negative class to the other classes. We end up with different classifiers, each of which is trained to recognize one of the classes. We make a prediction given a new input by running all the classifiers and picking the classifier that predicts a class with the highest probability. To formalize...