Skip to content

[MRG] Makes roc_auc_score and average_precision_score docstrings more explicit #9557

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 4 commits into from
Aug 22, 2017
Merged

[MRG] Makes roc_auc_score and average_precision_score docstrings more explicit #9557

Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
7b25c1e
Updates the roc_auc_score and average_precision_score docstrings
jrbourbeau Aug 15, 2017
abe8903
Updates y_true description in roc_curve docstring
jrbourbeau Aug 15, 2017
9011776
Rewords y_true description in roc_curve docstring
jrbourbeau Aug 16, 2017
98c7bac
Updates roc_auc_score and average_precision_score docstrings
jrbourbeau Aug 21, 2017
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
8 changes: 4 additions & 4 deletions sklearn/metrics/ranking.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -116,7 +116,7 @@ def average_precision_score(y_true, y_score, average="macro",
Parameters
----------
y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels in binary label indicators.
True binary labels (either {0, 1} or {-1, 1}).

y_score : array, shape = [n_samples] or [n_samples, n_classes]
Target scores, can either be probability estimates of the positive
Expand Down Expand Up @@ -200,7 +200,7 @@ def roc_auc_score(y_true, y_score, average="macro", sample_weight=None):
Parameters
----------
y_true : array, shape = [n_samples] or [n_samples, n_classes]
True binary labels in binary label indicators.
True binary labels (either {0, 1} or {-1, 1}).

y_score : array, shape = [n_samples] or [n_samples, n_classes]
Target scores, can either be probability estimates of the positive
Expand Down Expand Up @@ -438,8 +438,8 @@ def roc_curve(y_true, y_score, pos_label=None, sample_weight=None,
----------

y_true : array, shape = [n_samples]
True binary labels in range {0, 1} or {-1, 1}. If labels are not
binary, pos_label should be explicitly given.
True binary labels. If labels are not either {-1, 1} or {0, 1}, then
pos_label should be explicitly given.

y_score : array, shape = [n_samples]
Target scores, can either be probability estimates of the positive
Expand Down