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Is F1 score enough?
F1 score – F1 Score is the weighted average of Precision and Recall. Therefore, this score takes both false positives and false negatives into account. Intuitively it is not as easy to understand as accuracy, but F1 is usually more useful than accuracy, especially if you have an uneven class distribution.
What does F-score means?
The F-score, also called the F1-score, is a measure of a model’s accuracy on a dataset. The F-score is a way of combining the precision and recall of the model, and it is defined as the harmonic mean of the model’s precision and recall.
Why is the F1 score in the loss function?
The loss function provides not only a measure of model error, it is in the heart of the learning process defining how to best fit the data to achieve optimal goals. For some reason though, embeddding the F1-score in the loss function is not a common practice. Why is it unusual to have the F1-score in the loss function?
How can the F1-score help with dealing with class imbalance?
F1 = 2 * (PRE * REC) / (PRE + REC) What we are trying to achieve with the F1-score metric is to find an equal balance between precision and recall, which is extremely useful in most scenarios when we are working with imbalanced datasets (i.e., a dataset with a non-uniform distribution of class labels).
Can you optimize for the F1 score directly?
This paper(behind a paywall) appears to discuss a maximum F1 criterion. The thing to ask is if F1, which is harmmean(precision(x, y), recall(x, y)), is differentiable wrt x. I don’t know if it is, but that’s what you’ll need to calculate gradients and backpropagate.
How is soft F1 loss used in classification?
These two values almost complement to 1. Remember that the macro soft-F1 loss we defined was actually the macro of 1- soft-F1 that we needed to minimize. This is a first indicator that the macro soft-F1 loss is directly optimizing for our evaluation metric which is the macro F1-score @ threshold 0.5.