How Is F1 Score Calculated

F1 Score Formula:

\[ F1 = 2 \times \frac{Precision \times Recall}{Precision + Recall} \]

F1 Score:

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1. What Is F1 Score?

The F1 Score is the harmonic mean of precision and recall, providing a balanced measure of a model's performance in binary classification tasks. It combines both precision and recall into a single metric that considers both false positives and false negatives.

2. How Does The Calculator Work?

The calculator uses the F1 Score formula:

\[ F1 = 2 \times \frac{Precision \times Recall}{Precision + Recall} \]

Where:

\( Precision \) — Proportion of true positive predictions among all positive predictions
\( Recall \) — Proportion of true positive predictions among all actual positives

Explanation: The F1 Score ranges from 0 to 1, where 1 represents perfect precision and recall, and 0 represents the worst performance.

3. Importance Of F1 Score

Details: F1 Score is particularly useful when dealing with imbalanced datasets where one class significantly outnumbers the other. It provides a more comprehensive evaluation than accuracy alone in such scenarios.

4. Using The Calculator

Tips: Enter precision and recall values as proportions between 0 and 1. Both values must be valid (0 ≤ value ≤ 1). The calculator will compute the F1 Score automatically.

5. Frequently Asked Questions (FAQ)

Q1: When should I use F1 Score instead of accuracy?
A: Use F1 Score when dealing with imbalanced datasets or when both false positives and false negatives are important considerations.

Q2: What is a good F1 Score value?
A: Generally, F1 Score above 0.7 is considered good, above 0.8 is very good, and above 0.9 is excellent, though this depends on the specific application.

Q3: Can F1 Score be used for multi-class classification?
A: Yes, through macro-F1 or micro-F1 scores that aggregate performance across multiple classes.

Q4: What are the limitations of F1 Score?
A: F1 Score gives equal weight to precision and recall, which may not be appropriate for all applications where one metric is more important than the other.

Q5: How does F1 Score relate to other metrics?
A: F1 Score is related to the F-beta score family, where F1 is the special case when beta = 1, giving equal importance to precision and recall.