DOC roc_auc_score and average_precision_score explicit about binary input
@@ -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
@@ -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
@@ -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
