sklearn.datasets.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, optional (default=100)

The total number of points generated.

shufflebool, optional (default=True)

Whether to shuffle the samples.

noisedouble or None (default=None)

Standard deviation of Gaussian noise added to the data.

random_stateint, 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

Xarray of shape [n_samples, 2]

The generated samples.

yarray 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¶

Varying regularization in Multi-layer Perceptron¶

Compare Stochastic learning strategies for MLPClassifier¶

Feature discretization¶