DOC Clarify docs for `make_classification`
@@ -42,9 +42,10 @@ def make_classification(n_samples=100, n_features=20, n_informative=2,
"""Generate a random n-class classification problem.
This initially creates clusters of points normally distributed (std=1)
- about vertices of a `2 * class_sep`-sided hypercube, and assigns an equal
- number of clusters to each class. It introduces interdependence between
- these features and adds various types of further noise to the data.
+ about vertices of an `n_informative`-dimensional hypercube with sides of
+ length `2*class_sep` and assigns an equal number of clusters to each
+ class. It introduces interdependence between these features and adds
+ various types of further noise to the data.
Prior to shuffling, `X` stacks a number of these primary "informative"
features, "redundant" linear combinations of these, "repeated" duplicates
@@ -94,10 +95,13 @@ def make_classification(n_samples=100, n_features=20, n_informative=2,
exceeds 1.
flip_y : float, optional (default=0.01)
- The fraction of samples whose class are randomly exchanged.
+ The fraction of samples whose class are randomly exchanged. Larger
+ values introduce noise in the labels and make the classification
+ task harder.
class_sep : float, optional (default=1.0)
- The factor multiplying the hypercube dimension.
+ The factor multiplying the hypercube size. Larger values spread
+ out the clusters/classes and make the classification task easier.
hypercube : boolean, optional (default=True)
If True, the clusters are put on the vertices of a hypercube. If
