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  • Book Overview & Buying Designing Machine Learning Systems with Python
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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

By : David Julian, Vahid Mirjalili
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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

2 (1)
By: David Julian, Vahid Mirjalili

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 (11 chapters)
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1. Thinking in Machine Learning
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Index

Tree models

Tree models are ubiquitous in machine learning. They are naturally suited to divide and conquer iterative algorithms. One of the main advantages of decision tree models is that they are naturally easy to visualize and conceptualize. They allow inspection and do not just give an answer. For example, if we have to predict a category, we can also expose the logical steps that give rise to a particular result. Also tree models generally require less data preparation than other models and can handle numerical and categorical data. On the down side, tree models can create overly complex models that do not generalize to new data very well. Another potential problem with tree models is that they can become very sensitive to changes in the input data and, as we will see later, this problem can be mitigated against using them as ensemble learners.

An important difference between decision trees and the hypothesis mapping used in the previous section is that the tree model does not use internal...

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