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DOC updates for D2 log loss #28969

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May 7, 2024
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DOC updates for D2 log loss #28969

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1bc6189
DOC updates for D2 log loss
OmarManzoor May 7, 2024
38f953a
Correct the definition in the docstring
OmarManzoor May 7, 2024
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6 changes: 3 additions & 3 deletions sklearn/metrics/_classification.py
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Expand Up @@ -3277,10 +3277,10 @@ def d2_log_loss_score(y_true, y_pred, *, sample_weight=None, labels=None):
:math:`D^2` score function, fraction of log loss explained.

Best possible score is 1.0 and it can be negative (because the model can be
arbitrarily worse). A model that always uses the empirical mean of `y_true` as
constant prediction, disregarding the input features, gets a D^2 score of 0.0.
arbitrarily worse). A model that always predicts the per-class proportions
of `y_true`, disregarding the input features, gets a D^2 score of 0.0.

Read more in the :ref:`User Guide <d2_score>`.
Read more in the :ref:`User Guide <d2_score_classification>`.

.. versionadded:: 1.5

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3 changes: 2 additions & 1 deletion sklearn/metrics/tests/test_classification.py
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Expand Up @@ -3048,7 +3048,8 @@ def test_d2_log_loss_score():


def test_d2_log_loss_score_raises():
"""Test that d2_log_loss raises error on invalid input."""
"""Test that d2_log_loss_score raises the appropriate errors on
invalid inputs."""
y_true = [0, 1, 2]
y_pred = [[0.2, 0.8], [0.5, 0.5], [0.4, 0.6]]
err = "contain different number of classes"
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