Akansha Yadav shows how to calculate F1 score from a confusion matrix using the formula 2 × TP / (2 × TP + FN + FP). F1 score is the harmonic mean of precision and recall, and works better than accuracy for imbalanced datasets.
Let's see how to calculate the F1 score using the confusion matrix. So confusion matrix is this, and let's see what F1 score is. So F1 score is basically the hybrid matrix useful for unbalanced class. If you have unbalanced class, then F1 score will give you the value of model evaluation correctly. So to calculate it, it is 2 pro— 2— it's 2 true positive— 2 true positive divided plus FN plus FP. So you have to divide true positive with all 3 values and you will get the value of F1 score.
