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[WIP] PR2493 cont'd (multiple output scoring) #3456

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[WIP] PR2493 cont'd (multiple output scoring) #3456

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a329846
replayed manojs changes in pr2493 onto master
eickenberg Jul 20, 2014
5e64244
Added documentation
eickenberg Jul 21, 2014
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44 changes: 43 additions & 1 deletion doc/modules/model_evaluation.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -929,6 +929,14 @@ variance is estimated as follow:

The best possible score is 1.0, lower values are worse.

The :func:`explained_variance_score` function has an `output_weights` keyword
with two possible values `None` and 'uniform'. If the value provided is `None`,
then the explained variance score is calculated for each dimension separately
and a numpy array is returned. If the value given is `uniform`, the
explained variance error is averaged over each dimension with a weight of
`1 / n_outputs`. If the value given is `variance`, then the explained
variance of each output is weighted by the scale of the variable.

Here a small example of usage of the :func:`explained_variance_score`
function::

Expand All @@ -937,6 +945,14 @@ 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, output_weights=None)
... # doctest: +ELLIPSIS
array([ 0.967..., 1. ])
>>> explained_variance_score(y_true, y_pred, output_weights=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.990...

Mean absolute error
...................
Expand All @@ -954,6 +970,14 @@ and :math:`y_i` is the corresponding true value, then the mean absolute error

\text{MAE}(y, \hat{y}) = \frac{1}{n_{\text{samples}}} \sum_{i=0}^{n_{\text{samples}}-1} \left| y_i - \hat{y}_i \right|.


The :func:`mean_absolute_error` function has an `output_weights` keyword
with two possible values `None` and 'uniform'. If the value provided is
`None`, then the mean absolute error is calculated for each dimension
separately and a numpy array is returned. If the value given is `uniform`, the
mean absolute error is averaged over each dimension with a weight of
`1 / n_outputs`.

Here a small example of usage of the :func:`mean_absolute_error` function::

>>> from sklearn.metrics import mean_absolute_error
Expand All @@ -965,7 +989,11 @@ Here 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, output_weights=None)
array([ 0.5, 1. ])
>>> mean_absolute_error(y_true, y_pred, output_weights=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.849...


Mean squared error
Expand Down Expand Up @@ -1021,6 +1049,14 @@ over :math:`n_{\text{samples}}` is defined as

where :math:`\bar{y} = \frac{1}{n_{\text{samples}}} \sum_{i=0}^{n_{\text{samples}} - 1} y_i`.

The :func:`r2_score` function has an `output_weights` keyword with two possible
values `None` and 'uniform'. If the value provided is `None`, then the r2 score
is calculated for each dimension separately and a numpy array is returned.
If the value given is `uniform`, the r2 score is averaged over each dimension
with a weight of `1 / n_outputs`. If the value is `variance`, then the
output r2 scores are averaged weighted by the variances of the corresponding
variables, which corresponds to a global r2 score.

Here a small example of usage of the :func:`r2_score` function::

>>> from sklearn.metrics import r2_score
Expand All @@ -1032,6 +1068,12 @@ Here a small example of usage of the :func:`r2_score` function::
>>> y_pred = [[0, 2], [-1, 2], [8, -5]]
>>> r2_score(y_true, y_pred) # doctest: +ELLIPSIS
0.938...
>>> r2_score(y_true, y_pred, output_weights=None)
... # doctest: +ELLIPSIS
array([ 0.965..., 0.908...])
>>> r2_score(y_true, y_pred, output_weights=[0.3, 0.7])
... # doctest: +ELLIPSIS
0.925...


.. topic:: Example:
Expand Down
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