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You're reading from The Machine Learning Workshop - Second Edition

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Published inJul 2020

Reading LevelIntermediate

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ISBN-139781839219061

Edition2nd Edition

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Machine Learning

Author (1)

Hyatt Saleh

Evaluation Metrics

Model evaluation is indispensable for creating effective models that not only perform well on the data that was used to train the model but also on unseen data. The task of evaluating the model is especially easy when dealing with supervised learning problems, where there is a ground truth that can be compared against the prediction of the model.

Determining the accuracy percentage of the model is crucial for its application to unseen data that does not have a label class to compare to. For example, a model with an accuracy of 98% may allow the user to assume that the odds of having an accurate prediction are high, and hence the model should be trusted.

The evaluation of performance, as mentioned previously, should be done on the validation set (dev set) to fine-tune the model, and on the test set to determine the expected performance of the selected model on unseen data.

Evaluation Metrics for Classification Tasks

A classification task refers to a model...

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The Machine Learning Workshop - Second Edition

Published in: Jul 2020Publisher: PacktISBN-13: 9781839219061

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Author (1)

Hyatt Saleh

Hyatt Saleh discovered the importance of data analysis for understanding and solving real-life problems after graduating from college as a business administrator. Since then, as a self-taught person, she not only works as a machine learning freelancer for many companies globally, but has also founded an artificial intelligence company that aims to optimize everyday processes. She has also authored Machine Learning Fundamentals, by Packt Publishing.
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