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[MRG+2] rebasing pr/3474 (multioutput regression metrics) #4491

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[MRG+2] rebasing pr/3474 (multioutput regression metrics) #4491

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55 changes: 51 additions & 4 deletions doc/modules/model_evaluation.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -1085,8 +1085,31 @@ Regression metrics

The :mod:`sklearn.metrics` module implements several loss, score, and utility
functions to measure regression performance. Some of those have been enhanced
to handle the multioutput case: :func:`mean_absolute_error`,
:func:`mean_squared_error`, :func:`median_absolute_error` and :func:`r2_score`.
to handle the multioutput case: :func:`mean_squared_error`,
:func:`mean_absolute_error`, :func:`explained_variance_score` and
:func:`r2_score`.


These functions have an ``multioutput`` keyword argument which specifies the
way the scores or losses for each individual target should be averaged. The
default is ``'uniform_average'``, which specifies a uniformly weighted mean
over outputs. If an ``ndarray`` of shape ``(n_outputs,)`` is passed, then its
entries are interpreted as weights and an according weighted average is
returned. If ``multioutput`` is ``'raw_values'`` is specified, then all
unaltered individual scores or losses will be returned in an array of shape
``(n_outputs,)``.


The :func:`r2_score` and :func:`explained_variance_score` accept an additional
value ``'variance_weighted'`` for the ``multioutput`` parameter. This option
leads to a weighting of each individual score by the variance of the
corresponding target variable. This setting quantifies the globally captured
unscaled variance. If the target variables are of different scale, then this
score puts more importance on well explaining the higher variance variables.
``multioutput='variance_weighted'`` is the default value for :func:`r2_score`
for backward compatibility. This will be changed to ``uniform_average`` in the
future.


Explained variance score
-------------------------
Expand All @@ -1113,6 +1136,14 @@ function::
>>> y_pred = [2.5, 0.0, 2, 8]
>>> explained_variance_score(y_true, y_pred) # doctest: +ELLIPSIS
0.957...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> explained_variance_score(y_true, y_pred, multioutput='raw_values')
... # doctest: +ELLIPSIS
array([ 0.967..., 1. ])
>>> explained_variance_score(y_true, y_pred, multioutput=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.990...

Mean absolute error
-------------------
Expand Down Expand Up @@ -1141,7 +1172,11 @@ Here is a small example of usage of the :func:`mean_absolute_error` function::
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> mean_absolute_error(y_true, y_pred)
0.75

>>> mean_absolute_error(y_true, y_pred, multioutput='raw_values')
array([ 0.5, 1. ])
>>> mean_absolute_error(y_true, y_pred, multioutput=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.849...


Mean squared error
Expand Down Expand Up @@ -1232,8 +1267,20 @@ Here is a small example of usage of the :func:`r2_score` function::
0.948...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> r2_score(y_true, y_pred) # doctest: +ELLIPSIS
>>> r2_score(y_true, y_pred, multioutput='variance_weighted')
... # doctest: +ELLIPSIS
0.938...
>>> y_true = [[0.5, 1], [-1, 1], [7, -6]]
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> r2_score(y_true, y_pred, multioutput='uniform_average')
... # doctest: +ELLIPSIS
0.936...
>>> r2_score(y_true, y_pred, multioutput='raw_values')
... # doctest: +ELLIPSIS
array([ 0.965..., 0.908...])
>>> r2_score(y_true, y_pred, multioutput=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.925...


.. topic:: Example:
Expand Down
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