Precision and Recall: A Beginner's Guide
In this video, we will discuss precision and recall, two important metrics for evaluating the performance of machine learning models. Precision measures the fraction of predicted positives that are actually positive, while recall measures the fraction of actual positives that are predicted positive.
We will start by defining precision and recall with an example, then we will talk about how to calculate it using intuitive way as well as with traditional formula.
Finally, we will provide the information about what could be the good precision and recall for a machine learning use case.
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Precision and recall are two important metrics for evaluating the performance of machine learning models.
In other words, precision measures how accurate a model is, while recall measures how complete a model is. For example, a spam filter with high precision would correctly identify most spam emails, while a spam filter with high recall would identify most of the spam emails that are actually sent.
The trade-off between precision and recall is that increasing one metric often decreases the other. For example, a spam filter that is set to be very strict will have high precision, but it will also have low recall. This is because the filter will only identify emails that are very clearly spam, which means that it will miss some of the spam emails that are actually sent.
The best way to choose between precision and recall depends on the specific application. For example, if you are trying to prevent spam, then you might want to prioritize precision, because you don't want to accidentally send legitimate emails to the spam folder. However, if you are trying to diagnose a disease, then you might want to prioritize recall, because you don't want to miss any cases of the disease.
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Soumyabrata Roy LinkedIn: / soumyabratar