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Machine Learning for OpenCV 4

You're reading from   Machine Learning for OpenCV 4 Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn

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Product type Paperback
Published in Sep 2019
Publisher Packt
ISBN-13 9781789536300
Length 420 pages
Edition 2nd Edition
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Authors (4):
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Aditya Sharma Aditya Sharma
Author Profile Icon Aditya Sharma
Aditya Sharma
Michael Beyeler (USD) Michael Beyeler (USD)
Author Profile Icon Michael Beyeler (USD)
Michael Beyeler (USD)
Vishwesh Ravi Shrimali Vishwesh Ravi Shrimali
Author Profile Icon Vishwesh Ravi Shrimali
Vishwesh Ravi Shrimali
Michael Beyeler Michael Beyeler
Author Profile Icon Michael Beyeler
Michael Beyeler
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Fundamentals of Machine Learning and OpenCV FREE CHAPTER
2. A Taste of Machine Learning 3. Working with Data in OpenCV 4. First Steps in Supervised Learning 5. Representing Data and Engineering Features 6. Section 2: Operations with OpenCV
7. Using Decision Trees to Make a Medical Diagnosis 8. Detecting Pedestrians with Support Vector Machines 9. Implementing a Spam Filter with Bayesian Learning 10. Discovering Hidden Structures with Unsupervised Learning 11. Section 3: Advanced Machine Learning with OpenCV
12. Using Deep Learning to Classify Handwritten Digits 13. Ensemble Methods for Classification 14. Selecting the Right Model with Hyperparameter Tuning 15. Using OpenVINO with OpenCV 16. Conclusion 17. Other Books You May Enjoy

Scoring models using different evaluation metrics

So far, we have evaluated classification performance using accuracy (the fraction of correctly classified samples) and regression performance using R2. However, these are only two of the many possible ways to summarize how well a supervised model performs on a given dataset. In practice, these evaluation metrics might not be appropriate for our application, and it is important to choose the right metric when selecting between models and adjusting parameters.

When selecting a metric, we should always have the end goal of the machine learning application in mind. In practice, we are usually interested not just in making accurate predictions but also in using these predictions as part of a larger decision-making process. For example, minimizing false positives might be as equally important as maximizing accuracy.

We should pick the...
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