F1 Score 편집하기

The F1 Score is a classification metric that combines precision and recall into a single measure, providing a balanced assessment of a model’s accuracy in identifying positive instances. It is particularly useful when both false positives and false negatives are important to minimize.
==Definition==
The F1 Score is the harmonic mean of precision and recall, calculated as:
:'''<big>F1 Score = 2 * ([[Precision (Data Science)|Precision]] * [[Recall (Data Science)|Recall]]) / ([[Precision (Data Science)|Precision]] + [[Recall (Data Science)|Recall]])</big>'''
This metric ranges from '''0 to 1''', with a score closer to 1 indicating better model performance. The F1 Score emphasizes the balance between precision and recall, making it suitable when both metrics are critical.
==Importance of the F1 Score==
The F1 Score is valuable in scenarios where:
*Both false positives and false negatives are costly
*The dataset is imbalanced, and accuracy alone would not provide a clear measure of performance
*The goal is to achieve a trade-off between precision and recall
==When to Use the F1 Score==
The F1 Score is most appropriate when:
*There is a need to balance precision and recall, such as in medical diagnosis or fraud detection
*Neither false positives nor false negatives can be ignored
==Limitations of the F1 Score==
While the F1 Score is a balanced metric, it has limitations:
*It does not distinguish between precision and recall, which may be undesirable when one is more important than the other
*It can be less informative in cases where class distribution is extremely imbalanced
==Alternative Metrics==
When the F1 Score alone is not sufficient, consider other metrics to complement the evaluation:
*'''Precision''': Focuses on the accuracy of positive predictions, suitable when false positives are costly.
*'''Recall''': Focuses on the completeness of positive predictions, important when false negatives are costly.
*'''AUC-ROC''': Provides a more comprehensive view across different thresholds for positive classification.
==See Also==
*[[Precision (Data Science)|Precision]]
*[[Recall (Data Science)|Recall]]
*[[Accuracy (Data Science)|Accuracy]]
*[[Confusion Matrix]]
*[[Classification Metrics]]
[[Category:Data Science]]