sklearn.datasets.make_moons

sklearn.datasets.make_moons(n_samples=100, shuffle=True, noise=None, random_state=None)

Make two interleaving half circles

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

Parameters:

n_samples : int, optional (default=100)

The total number of points generated.

shuffle : bool, optional (default=True)

Whether to shuffle the samples.

noise : double or None (default=None)

Standard deviation of Gaussian noise added to the data.

random_state : int, RandomState instance or None (default)

Determines random number generation for dataset shuffling and noise. Pass an int for reproducible output across multiple function calls. See Glossary.

Returns:

X : array of shape [n_samples, 2]

The generated samples.

y : array of shape [n_samples]

The integer labels (0 or 1) for class membership of each sample.

Examples using sklearn.datasets.make_moons

Comparing anomaly detection algorithms for outlier detection on toy datasets

Classifier comparison

Comparing different hierarchical linkage methods on toy datasets

Comparing different clustering algorithms on toy datasets

Compare Stochastic learning strategies for MLPClassifier

Varying regularization in Multi-layer Perceptron

Feature discretization