What are Precision and Recall in Machine Learning?

Machine learning models will make mistakes, but exactly the kind of mistake your specific use case can tolerate is where precision and recall come in.

If your dataset is not well balanced or mistakes have a varying impact, your model's accuracy is not a good measure for performance.

Whenever you are looking for specific information, the main task is often to differentiate between the relevant data you are looking for and the irrelevant information that clouds your view.

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Therefore, it is more important to analyze model performance concerning relevant elements and not the overall dataset.

Key takeaways:

👉 The precision of a model describes how many detected items are truly relevant. It is calculated by dividing the true positives by overall positives.

👉 Recall is a measure of how many relevant elements were detected. Therefore it divides true positives by the number of relevant elements.

We go into the full lowdown of exactly what precision is vs recall, together with tangible examples to bring the concepts to life. We'll also introduce you to the idea of the F-measure in machine learning - a neat way of combining both relevancy measures in one number.

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