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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

By : David Julian
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Designing Machine Learning Systems with Python

Designing Machine Learning Systems with Python

2 (1)
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 (11 chapters)
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1. Thinking in Machine Learning
10
Index

Model selection


There are a number of hyper parameters that can be adjusted to improve performance. It is often not a straightforward process, determining the effect of the various parameters, both individually and in combination with each other. Common things to try include getting more training examples, adding or removing features, adding polynomial features, and increasing or decreasing the regularization parameter. Given that we can spend a considerable amount of time collecting more data, or manipulating data in other ways, it is important that the time you spend is likely to result in a productive outcome. One of the most important ways to do this is using a process known as grid search.

Gridsearch

The sklearn.grid_search.GridSearchCV object is used to perform an exhaustive search on specified parameter values. This allows iteration through defined sets of parameters and the reporting of the result in the form of various metrics. The important parameters for GridSearchCV objects are...

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