July 2017
Beginner to intermediate
715 pages
17h 3m
English
Content preview from Java: Data Science Made Easy
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Precision, recall, and F1
In some cases, accuracy values are deceiving: they suggest that the classifier is good, although it is not. For example, suppose we have an unbalanced dataset: there are only 1% of examples that are positive, and the rest (99%) are negative. Then, a model that always predicts negative is right in 99% of the cases, and hence will have an accuracy of 0.99. But this model is not useful.
There are alternatives to accuracy that can overcome this problem. Precision and recall are among these metrics, as they both look at the fraction of positive items that the model correctly recognized. So, if we have a large number of negative examples, we can still perform some meaningful evaluation of the model.
Precision and recall ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.