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)

Evaluating a model

Model evaluation strategies come in many different forms and shapes. In the following sections, we will, therefore, highlight three of the most commonly used techniques to compare models against each other:

  • k-fold cross-validation
  • bootstrapping
  • McNemar's test

In principle, model evaluation is simple: after training a model on some data, we can estimate its effectiveness by comparing model predictions to some ground truth values. We learned early on that we should split the data into a training and a test set, and we tried to follow this instruction whenever possible. But why exactly did we do that again?

Evaluating a model the wrong way

The reason we never evaluate a model on the training set is that...