Learn about F1 Score, including how to measure it, and leverage it in dashboards and visualizations with Metabase.
An F1 score is a metric used in machine learning for classification models. To be more specific, an F1 score measures errors by calculating the harmonic mean of precision and recall. This score is meant to tell you accurate results for both imbalanced and balanced datasets using precision and recall calculations. F1 scores are on a scale of 0 to 1, 1 being the best possible score. Combining recall and precision to get your F1 score gives you the ability to see whether or not your model is balanced when capturing and predicting positive cases. Visualizing your F1 score can show you where you’re at with your classification models and machine learning.
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Your F1 score requires that you know your precision and recall numbers. You can figure those out by doing the following calculations for a given dataset: Precision: number of true positives / (number of true positives + number of false positives) Recall: number of true positives / (number of true positives + number of true negatives) Once you have these calculations, you can calculate your F1 score. Remember, the goal is to identify balance between these two calculations. You’ll calculate your F1 score like this: 2 x (precision x recall) / (precision + recall) = F1 score
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