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ML model performance metrics

ML model performance metrics - Amazon Web Services (AWS) Tutorial

From the course: AWS Certified AI Practitioner (AIF-C01) Cert Prep

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ML model performance metrics

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- When discussing machine learning model performance metrics, unfortunately, there has to be a conversation about mathematics. But first, let's define some explicit terms that we're going to use. Accuracy, precision, and recall. Accuracy is a simple formula. It's calculated by dividing the model's correct predictions by the total number of predictions. Pretty straightforward. Precision is the proportion of positive predictions that are actually correct, so it's considering false positives and false negatives. Third, we have recall, which is calculating by dividing the true positive count by the sum of true positives and false negatives. And each of these can mean different things. And so, let's look at some actual metrics that we can use to help evaluate the performance of a model. The first is an F1 score, and this is defined as the harmonic mean of the precision and recall that give you a balance between the two. Now, the use cases for this is identifying if the class distribution…

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