Question - What is F1-score and How Is It Used?
Answer -
F-score or F1-score is a measure of overall accuracy of a binary classification model. Before understanding F1-score, it is crucial to understand two more measures of accuracy, i.e., precision and recall.
Precision is defined as the percentage of True Positives to the total number of positive classifications predicted by the model. In other words,
Precision = (No. of True Positives / No. True Positives + No. of False Positives)
Recall is defined as the percentage of True Positives to the total number of actual positive labeled data passed to the model. In other words,
Precision = (No. of True Positives / No. True Positives + No. of False Negatives)
Both precision and recall are partial measures of accuracy of a model. F1-score combines precision and recall and provides an overall score to measure a model’s accuracy.
F1-score = 2 × (Precision × Recall) / (Precision + Recall)
This is why, F1-score is the most popular measure of accuracy in any Machine-Learning-based binary classification model.