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

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

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.