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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

Combining decision trees into a random forest

A popular variation of bagged decision trees are the so-called random forests. These are essentially a collection of decision trees, where each tree is slightly different from the others. In contrast to bagged decision trees, each tree in a random forest is trained on a slightly different subset of data features.

Although a single tree of unlimited depth might do a relatively good job of predicting the data, it is also prone to overfitting. The idea behind random forests is to build a large number of trees, each of them trained on a random subset of data samples and features. Because of the randomness of the procedure, each tree in the forest will overfit the data in a slightly different way. The effect of overfitting can then be reduced by averaging the predictions of the individual trees.

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