> ## Documentation Index
> Fetch the complete documentation index at: https://www.getmaxim.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# F1 Score

> Calculates the harmonic mean of precision and recall, providing a single, balanced score that is useful for imbalanced datasets.

### Input

* **`output`** (str): The generated text (set of items)
* **`expectedOutput`** (str): The reference text (set of items)

### Output

* **`Result`** (float): A score between 0 and 1.

## Interpretation

* **Higher scores (closer to 1)**: Strong balance of precision and recall
* **Lower scores (closer to 0)**: Either precision or recall (or both) are weak

## Formula

$$
\mathrm{F1} = \frac{2 \cdot \mathrm{Precision} \cdot \mathrm{Recall}}{\mathrm{Precision} + \mathrm{Recall}}
$$

Where:

$$
\mathrm{Precision} = \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FP}},\quad\mathrm{Recall} = \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN}}
$$

<Note>This is a **Similarity** Metric</Note>

## Use Cases

* Evaluating classifiers with class imbalance
* Information extraction and NER
* Scenarios where both precision and recall matter