DOC minor update to make_classification about flip_y
@@ -99,13 +99,14 @@ def make_classification(n_samples=100, n_features=20, *, n_informative=2,
classes are balanced. Note that if ``len(weights) == n_classes - 1``,
then the last class weight is automatically inferred.
More than ``n_samples`` samples may be returned if the sum of
- ``weights`` exceeds 1.
+ ``weights`` exceeds 1. Note that the actual class proportions will
+ not exactly match ``weights`` when ``flip_y`` isn't 0.
flip_y : float, default=0.01
The fraction of samples whose class is assigned randomly. Larger
values introduce noise in the labels and make the classification
task harder. Note that the default setting flip_y > 0 might lead
- to less than n_classes in y in some cases.
+ to less than ``n_classes`` in y in some cases.
class_sep : float, default=1.0
The factor multiplying the hypercube size. Larger values spread
