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DOC improve conventions used in MAPE #30012

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Oct 8, 2024
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DOC improve conventions used in MAPE #30012

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0884659
Fix MAPE documentation: clarify output range and provide examples
Rachit23110261 Oct 5, 2024
ef34ec7
Merge branch 'main' into fix-mape-documentation
Rachit23110261 Oct 5, 2024
77904e5
Fix MAPE docstring: clarify output range and example
Rachit23110261 Oct 7, 2024
83be042
Fix MAPE docstring: clarify output range and example
Rachit23110261 Oct 7, 2024
73fa586
fix docstring and narrative
glemaitre Oct 8, 2024
71f1446
Update sklearn/metrics/_regression.py
glemaitre Oct 8, 2024
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10 changes: 5 additions & 5 deletions doc/modules/model_evaluation.rst
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Expand Up @@ -2489,11 +2489,11 @@ relative percentage error with respect to actual output.

.. note::

The MAPE formula here represents a relative error and outputs a value in the
range [0, 1]. It is not a percentage in the range [0, 100] and a value of 100
does not mean 100% but 1e2. The motivation for the MAPE formula here to be in
the range [0, 1] is to be consistent with other error metrics in scikit-learn
such as `accuracy_score`.
The MAPE formula here does not represent the common "percentage" definition: the
percentage in the range [0, 100] is converted to a relative value in the range [0,
1] by dividing by 100. Thus, an error of 200% corresponds to a relative error of 2.
The motivation here is to have a range of values that is more consistent with other
error metrics in scikit-learn, such as `accuracy_score`.

To obtain the mean absolute percentage error as per the Wikipedia formula,
multiply the `mean_absolute_percentage_error` computed here by 100.
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11 changes: 5 additions & 6 deletions sklearn/metrics/_regression.py
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Expand Up @@ -330,12 +330,11 @@ def mean_absolute_percentage_error(
):
"""Mean absolute percentage error (MAPE) regression loss.

Note here that the output is not a percentage in the range [0, 100]
and a value of 100 does not mean 100% but 1e2. Furthermore, the output
can be arbitrarily high when `y_true` is small (which is specific to the
metric) or when `abs(y_true - y_pred)` is large (which is common for most
regression metrics). Read more in the
:ref:`User Guide <mean_absolute_percentage_error>`.
Note that we are not using the common "percentage" definition: the percentage
in the range [0, 100] is converted to a relative value in the range [0, 1]
by dividing by 100. Thus, an error of 200% corresponds to a relative error of 2.

Read more in the :ref:`User Guide <mean_absolute_percentage_error>`.

.. versionadded:: 0.24

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