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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
10
Index

Bagging


Bagging, also called bootstrap aggregating, comes in a few flavors and these are defined by the way they draw random subsets from the training data. Most commonly, bagging refers to drawing samples with replacement. Because the samples are replaced, it is possible for the generated datasets to contain duplicates. It also means that data points may be excluded from a particular generated dataset, even if this generated set is the same size as the original. Each of the generated datasets will be different and this is a way to create diversity among the models in an ensemble. We can calculate the probability that a data point is not selected in a sample using the following example:

Here, n is the number of bootstrap samples. Each of the n bootstrap samples results in a different hypothesis. The class is predicted either by averaging the models or by choosing the class predicted by the majority of models. Consider an ensemble of linear classifiers. If we use majority voting to determine...

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