There are a rich variety of ways in which we can both transform and construct new features to make our models work more efficiently and give more accurate results. In general, there are no hard and fast rules for deciding which of the methods to use for a particular model. Much depends on the feature types (quantitative, ordinal, or categorical) that you are working with. A good first approach is to normalize and scale the features, and if the model requires it, transform the feature to an appropriate type, as we do through discretization. If the model performs poorly, it may be necessary to apply further preprocessing such as PCA. In the next chapter, we will look at ways in which we can combine different types of models, through the use of ensembles, to improve performance and provide greater predictive power.
Designing Machine Learning Systems with Python
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Designing Machine Learning Systems with Python
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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.
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Table of Contents (16 chapters)
Designing Machine Learning Systems with Python
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Thinking in Machine Learning
Tools and Techniques
Turning Data into Information
Models – Learning from Information
Linear Models
Neural Networks
Features – How Algorithms See the World
Learning with Ensembles
Design Strategies and Case Studies
Index
Customer Reviews