---
title: regression_evaluate
datasets:
- GeoBenchmark
tags:
- evaluate
- metric
description: 'TODO: add a description here'
sdk: gradio
sdk_version: 6.5.1
app_file: app.py
pinned: false
---

# Metric Card for regression_evaluate

## Metric Description

This metric aims to evaluate regression tasks done by LMs. It expects the model to generate a list of numerical values to compare it to gold list of numerical values.

## How to Use

This metric takes 2 mandatory arguments : `generations` (a list of string), `golds` (a list of list of floats).

```python
import evaluate
metric = evaluate.load("rfr2003/regression_evaluate")
results = metric.compute(generations=['[150, 0]'], golds=[183, 177, 146, 85, 70, 78, 55, 17, 0, -1, -1])
print(results)
{'precision': [4.0], 'recall': [344.0], 'macro-mean': [174.0], 'median macro-mean': 174.0}
```

This metric accepts one optional argument:

`d`: function used to compute the distance between a generated value and a gold one. The default value is a function computing the absolute difference between two numbers.

### Output Values

This metric outputs a dictionary with the following values:

`precision`: Sum of the minimum distances between each predicted value and the set of gold values, computed for each question.

`recall`: Sum of the minimum distances between each gold value and the set of generated values, computed for each question.

`macro-mean`: Average between precision and recall, computed for each question.

`median macro-mean`: Median accross macro-mean values.

#### Values from Popular Papers

### Examples

```python
import evaluate
metric = evaluate.load("rfr2003/regression_evaluate")
results = metric.compute(generations=['[150, 0]'], golds=[183, 177, 146, 85, 70, 78, 55, 17, 0, -1, -1])
print(results)
{'precision': [4.0], 'recall': [344.0], 'macro-mean': [174.0], 'median macro-mean': 174.0}
```

## Limitations and Bias

## Citation

## Further References