make_moons#

sklearn.datasets.make_moons(n_samples=100, *, shuffle=True, noise=None, random_state=None)[source]#

Make two interleaving half circles.

A simple toy dataset to visualize clustering and classification algorithms. Read more in the User Guide.

Parameters:

n_samplesint or tuple of shape (2,), dtype=int, default=100: If int, the total number of points generated. If two-element tuple, number of points in each of two moons.

Changed in version 0.23: Added two-element tuple.
shufflebool, default=True: Whether to shuffle the samples.
noisefloat, default=None: Standard deviation of Gaussian noise added to the data.
random_stateint, RandomState instance or None, default=None: Determines random number generation for dataset shuffling and noise. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns:

Xndarray of shape (n_samples, 2): The generated samples.
yndarray of shape (n_samples,): The integer labels (0 or 1) for class membership of each sample.

Examples

>>> from sklearn.datasets import make_moons
>>> X, y = make_moons(n_samples=200, noise=0.2, random_state=42)
>>> X.shape
(200, 2)
>>> y.shape
(200,)

Gallery examples#