What’s the F1 score?
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Pragya Rani
Answers ( 5 )
The F1 score is a measure of a model’s performance. It is a weighted average of the precision and recall of a model, with results tending to 1 being the best, and those tending to 0 being the worst. You would use it in classification tests where true negatives don’t matter much.
The F1 score is the harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 (perfect precision and recall). We generally prefer this when the dataset is imbalanced
F1 Score is one of the metric which we use while measuring the strength of
our models in a classification setting.
F1 = 2*Precision*Recall/(Precision + Recall)
F1 score is a good metric to use when you need a balance between precision and recall
and the data is highly imbalanced i.e. you have large no of actual negatives.
Precision is, out of the total positive predicted, how many are really positive.
Recall is, out of the total positives, how many were predicted as positives.
The F1 score is the harmonic mean of the precision and recall.
F1 = 2*Precision*Recall/(Precision + Recall)
It is model evaluation metric for classification problems when we are dealing with imbalanced data set. It is the harmonic mean of precision and recall.
F1 = (2*(Precision*Recall))/(Precision + Recall)
Higher the F1 score better the model.
F1=2*(precision +recall) /precision *recall
It is harmonic mean if precision and recall. It is a measure of goodness of model. It is used in case of imbalanced dataset where accuracy score can be deceiving. Like in cancer prediction model where patients suffering from cancer Are only 1% of entire data so if model always predict patient is not suffering from cancer then accuracy will be 99% but model is of no use. It is used in classification problems