We need to select whether to use averaging or not based on the problem at hand. Compute the F1 score, also known as balanced F-score or F-measure. Imagine that you're taking a class where the instructor thinks homework and tests are the most important part of the class. The goal of the F1 score is to combine the precision and recall metrics into a single metric. . Which factors would be prioritized depends on the product or project, though cost benefits or ROI are the most important. (you sum the number of true positives / false negatives for each class). Read More: Assigning Weights to Variables in Excel (3 Useful Examples). We have an AI which is trained to recognize which apples are ripe for picking, and pick all the ripe apples and no unripe apples. Lets follow the steps below to complete the calculation in Excel. and a formula for the general F equation, allowing the user to grant varying weights to precision or recall would be:=((1+(C9^2))*((A9*B9)/((C9^2*A9)+B9))) . We find that there are now two false positives and only one false negative, while the number of true positives and true negatives remained the same. Generally, it represents the basic formula. Precision, Recall, and F1 Score of Multiclass Classification Learn in Depth. But since the metric required is weighted-f1, I am not sure if categorical_crossentropy is the best loss choice.
weighted f1 score formula