Book Image

Machine Learning for OpenCV

By : Michael Beyeler
Book Image

Machine Learning for OpenCV

By: Michael Beyeler

Overview of this book

Machine learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of machine learning, with Deep Learning driving innovative systems such as self-driving cars and Google’s DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and machine learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression. Once all the basics are covered, you will start exploring various algorithms such as decision trees, support vector machines, and Bayesian networks, and learn how to combine them with other OpenCV functionality. As the book progresses, so will your machine learning skills, until you are ready to take on today's hottest topic in the field: Deep Learning. By the end of this book, you will be ready to take on your own machine learning problems, either by building on the existing source code or developing your own algorithm from scratch!
Table of Contents (13 chapters)

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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