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`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 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 using `sklearn.datasets.make_moons`¶

Classifier comparison — Classifier comparison¶

Comparing different hierarchical linkage methods on toy datasets — Comparing different hierarchical linkage methods on toy datasets¶

Comparing different clustering algorithms on toy datasets — Comparing different clustering algorithms on toy datasets¶

Comparing anomaly detection algorithms for outlier detection on toy datasets — Comparing anomaly detection algorithms for outlier detection on toy datasets¶

Statistical comparison of models using grid search — Statistical comparison of models using grid search¶

Compare Stochastic learning strategies for MLPClassifier — Compare Stochastic learning strategies for MLPClassifier¶

Varying regularization in Multi-layer Perceptron — Varying regularization in Multi-layer Perceptron¶

Feature discretization — Feature discretization¶