What's the F1 score? How would you use it?

 What's the F1 score?

The F1-score combines the precision and recall of a classifier into a single metric by taking their harmonic mean. It's mainly used to compare the results of two different classifiers. Assume that classifier A has a higher recall, and classifier B has higher precision. In this instance, The F1-scores for both classifiers can be used to see which one performs better. 

A classification model's F1-score is calculated as follows:

F1-Score=2(precisionrecal)/(precision + recal)

How would you use it?

It assesses the model data set's accuracy. Although accuracy is brutal to understand, the concept of the F1-score becomes more valuable in cases of unequal class distribution. Many machine learning models make use of it. It is used when false Negative and False Positives are more crucial in the data set than True positives and true negatives. It provides better results where the data is incorrectly classified.