---
title: Average Precision
tags:
- evaluate
- metric
description: "Average precision score."
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
---

# Metric Card for Average Precision

## How to Use
```python
import evaluate

metric = evaluate.load("chanelcolgate/average_precision")
results = metric.compute(references=references, prediction_scores=prediction_scores)
```

### Inputs
- **y_true** (`ndarray` of shape (n_samples,) or (n_samples, n_classes)): True binary labels or binary label indicators.
- **y_score** (`ndarray` of shape (n_samples,) or (n_samples, n_classes)):
Target scores, can either be probability estimates of the positive class, confidence values, or non-thresholded measure of decisions (as returned by :term:`decision_function` on some classifiers).
- **average**: {'micro', 'samples', 'weighted', 'macro'} or None, default='macro`

    If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:

    ``'micro'``:
        Calculate metrics globally by considering each element of the label indicator matrix as a label.

    ``'macro'``:
        Calculate metrics for each label, and find their unweighted mean This does not take label imbalance into account.
        
    ``'weighted'``:
        Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label).

    ``'samples'``:
        Calculate metrics for each label, and find their average
    Will be ignored when ``y_true`` is binary.
- **pos_label** (`int` or `str`, default=1): The label of the positive class. Only applied to binary ``y_true``. For multilabel-indicator ``y_true``, ``pos_label`` is fixed to 1.
- **sample_weight** (`array-like` of shape (n_samples,), default=None): Sample weights.

### Output Values

*Explain what this metric outputs and provide an example of what the metric output looks like. Modules should return a dictionary with one or multiple key-value pairs, e.g. {"bleu" : 6.02}*

*State the range of possible values that the metric's output can take, as well as what in that range is considered good. For example: "This metric can take on any value between 0 and 100, inclusive. Higher scores are better."*

#### Values from Popular Papers
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### Examples
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## Limitations and Bias
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## Citation
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## Further References
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