metadata

title: MSE
emoji: 🤗
colorFrom: blue
colorTo: red
sdk: gradio
sdk_version: 3.19.1
app_file: app.py
pinned: false
tags:
  - evaluate
  - metric
description: >-
  Mean Squared Error(MSE) is the average of the square of difference between the
  predicted and actual values.

Metric Card for MSE

Metric Description

Mean Squared Error(MSE) represents the average of the squares of errors -- i.e. the average squared difference between the estimated values and the actual values.

How to Use

At minimum, this metric requires predictions and references as inputs.

>>> mse_metric = evaluate.load("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)

Inputs

Mandatory inputs:

predictions: numeric array-like of shape (n_samples,) or (n_samples, n_outputs), representing the estimated target values.
references: numeric array-like of shape (n_samples,) or (n_samples, n_outputs), representing the ground truth (correct) target values.

Optional arguments:

sample_weight: numeric array-like of shape (n_samples,) representing sample weights. The default is None.
multioutput: raw_values, uniform_average or numeric array-like of shape (n_outputs,), which defines the aggregation of multiple output values. The default value is uniform_average.
- raw_values returns a full set of errors in case of multioutput input.
- uniform_average means that the errors of all outputs are averaged with uniform weight.
- the array-like value defines weights used to average errors.
squared (bool): If True returns MSE value, if False returns RMSE (Root Mean Squared Error). The default value is True.

Output Values

This metric outputs a dictionary, containing the mean squared error score, which is of type:

float: if multioutput is uniform_average or an ndarray of weights, then the weighted average of all output errors is returned.
numeric array-like of shape (n_outputs,): if multioutput is raw_values, then the score is returned for each output separately.

Each MSE float value ranges from 0.0 to 1.0, with the best value being 0.0.

Output Example(s):

{'mse': 0.5}

If multioutput="raw_values":

{'mse': array([0.41666667, 1. ])}

Values from Popular Papers

Examples

Example with the uniform_average config:

>>> mse_metric = evaluate.load("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> results = mse_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'mse': 0.375}

Example with squared = True, which returns the RMSE:

>>> mse_metric = evaluate.load("mse")
>>> predictions = [2.5, 0.0, 2, 8]
>>> references = [3, -0.5, 2, 7]
>>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
>>> print(rmse_result)
{'mse': 0.6123724356957945}

Example with multi-dimensional lists, and the raw_values config:

>>> mse_metric = evaluate.load("mse", "multilist")
>>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
>>> references = [[0, 2], [-1, 2], [8, -5]]
>>> results = mse_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
>>> print(results) 
{'mse': array([0.41666667, 1. ])}
"""

Limitations and Bias

MSE has the disadvantage of heavily weighting outliers -- given that it squares them, this results in large errors weighing more heavily than small ones. It can be used alongside MAE, which is complementary given that it does not square the errors.

Citation(s)

@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}

@article{willmott2005advantages,
  title={Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance},
  author={Willmott, Cort J and Matsuura, Kenji},
  journal={Climate research},
  volume={30},
  number={1},
  pages={79--82},
  year={2005}
}

Further References

Mean Squared Error - Wikipedia