What is the range of values for the F1 Score?a)0 to 100b)0 to 10c)0 to...
Understanding the F1 Score
The F1 Score is a crucial metric in evaluating the performance of a classification model, especially in scenarios with imbalanced datasets. It combines two other metrics: precision and recall.
Range of the F1 Score
- The F1 Score ranges from 0 to 1.
- A score of 0 indicates the worst performance, meaning the model has failed to make any correct predictions.
- A score of 1 signifies perfect precision and recall, which means the model has made all the correct predictions without any false positives or false negatives.
Why the Range is 0 to 1?
- The F1 Score is calculated as the harmonic mean of precision and recall, which are both fractions (i.e., values between 0 and 1).
- Since both precision and recall can only take values between 0 and 1, the F1 Score, which is derived from these two metrics, must also fall within the same bounds.
Interpretation of Scores
- F1 Score = 0: No positive predictions were correctly identified.
- F1 Score = 0.5: There is a balance but not an ideal performance.
- F1 Score = 1: The best possible outcome with no errors.
Conclusion
Understanding that the F1 Score ranges from 0 to 1 helps in appropriately interpreting model performance. It is a vital tool for making informed decisions in various applications of machine learning and data science.
What is the range of values for the F1 Score?a)0 to 100b)0 to 10c)0 to...
The range of values for the F1 Score is from 0 to 1. It is a decimal value that indicates the balance between Precision and Recall, with 1 being the ideal value.