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Is High F measure good?
Following our Data Science principles, I came up with a simple first version optimizing for its F1 score , the most-recommended quality measure for such a binary classification problem. A binary classification task. Clearly, the higher the F1 score the better, with 0 being the worst possible and 1 being the best.
What does the F-score tell you?
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.
What’s the maximum value for the F measure?
For F-measure, the maximum possible is 1, and you loose resistance as soon as one among he two looses resistance as well (that is too say, get a value below 1). If you want to understand better this quantity and its dynamic, think about the physic phenomenon. For example, it appears that the F-measure <= max (sensitivity, precision).
Which is the best value for the F score?
The F score reaches the best value, meaning perfect precision and recall, at a value of 1. The worst F score, which means lowest precision and lowest recall, would be a value of 0. The F score is used to measure a test’s accuracy, and it balances the use of precision and recall to do it.
What is the harmonic mean of the F-measure?
$\\begingroup$. The F-measure is the harmonic mean of your precision and recall. In most situations, you have a trade-off between precision and recall. If you optimize your classifier to increase one and disfavor the other, the harmonic mean quickly decreases.
What is the sweet spot for the F measure?
Experiments indicate that the sweet spot here is around 0.76, where the F-measure is 0.87. The F-measure is the harmonic mean of your precision and recall. In most situations, you have a trade-off between precision and recall. If you optimize your classifier to increase one and disfavor the other, the harmonic mean quickly decreases.