metadata

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

import evaluate

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

Inputs

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

Give examples, preferrably with links to leaderboards or publications, to papers that have reported this metric, along with the values they have reported.

Examples

Give code examples of the metric being used. Try to include examples that clear up any potential ambiguity left from the metric description above. If possible, provide a range of examples that show both typical and atypical results, as well as examples where a variety of input parameters are passed.

Limitations and Bias

Note any known limitations or biases that the metric has, with links and references if possible.

Citation

Cite the source where this metric was introduced.

Further References

Add any useful further references.