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  • Abbreviations (Image by author) Using the weighted mean, we can easily obtain the measure F2 : This measure allows us to define how much the recall is important than the precision. Using the F beta measure in the sklearn is very easy, just follow the example: Further reading. where Precision can be defined as the probability of positive predictions that are actual members of the positive class. Output: 0.8585858585858585. print(fbeta_score(y_test, y_pred, beta=0.5)) print(fbeta_score(y_test, y_pred, beta=1)) print(fbeta_score(y_test, y_pred, beta=2)) It is evident in the above graph that as we increase our beta value from 0, the curve starts moving towards the recall curve, which means with an increase in the beta value gives more importance to recall, and the below code to plot the F-measure at various beta and threshold values. The F1 score can be interpreted as a harmonic mean of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is: labels array-like, default=None. Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. Returns : f1_score float or array of float, shape = [n_unique_labels] >>> # binary classification >>> y_true_empty = [ 0 , 0 , 0 , 0 , 0 , 0 ] >>> y_pred_empty = [ 0 , 0 , 0 , 0 , 0 , 0 ] >>> f1_score ( y_true_empty , y_pred_empty ) 0.0.

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