Author(s): Vlastimil Martinek
Originally published on Towards AI.
TL;DR at the end
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Our job is to create a model to classify if people are healthy or sick. We are given data about them, we’ve created multiple classification models, and it’s time to select the best one.
A common way to estimate a model's performance is to measure its precision and recall.
Precision — What portion of all predicted positives are actual positives.
Recall — what portion of all actual positives in our data did we predict correctly.
Precision and recall are great metrics, but they’re still two numbers. If you want… Read the full blog for free on Medium.
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Published via Towards AI