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Machine Learning for OpenCV

You're reading from   Machine Learning for OpenCV Intelligent image processing with Python

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781783980284
Length 382 pages
Edition 1st Edition
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Authors (2):
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Michael Beyeler Michael Beyeler
Author Profile Icon Michael Beyeler
Michael Beyeler
Michael Beyeler (USD) Michael Beyeler (USD)
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Michael Beyeler (USD)
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Table of Contents (13) Chapters Close

Preface 1. A Taste of Machine Learning 2. Working with Data in OpenCV and Python FREE CHAPTER 3. First Steps in Supervised Learning 4. Representing Data and Engineering Features 5. Using Decision Trees to Make a Medical Diagnosis 6. Detecting Pedestrians with Support Vector Machines 7. Implementing a Spam Filter with Bayesian Learning 8. Discovering Hidden Structures with Unsupervised Learning 9. Using Deep Learning to Classify Handwritten Digits 10. Combining Different Algorithms into an Ensemble 11. Selecting the Right Model with Hyperparameter Tuning 12. Wrapping Up

Using Decision Trees to Make a Medical Diagnosis

Now that we know how to handle data in all shapes and forms, be it numerical, categorical, text, or image data, it is time to put our newly gained knowledge to good use.

In this chapter, we will learn how to build a machine learning system that can make a medical diagnosis. We aren't all doctors, but we've probably all been to one at some point in our lives. Typically, a doctor would gain as much information as possible about a patient's history and symptoms in order to make an informed diagnosis. We will mimic a doctor's decision-making process with the help of what is known as decision trees.

A decision tree is a simple yet powerful supervised learning algorithm that resembles a flow chart; we will talk more about this in just a minute. Other than in medicine, decision trees are commonly used in fields such...

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