This is documentation for an old release of Scikit-learn (version 1.3). Try the latest stable release (version 1.6) or development (unstable) versions.
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
¶
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Comparing different clustering algorithms on toy datasets
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Comparing different hierarchical linkage methods on toy datasets
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Comparing anomaly detection algorithms for outlier detection on toy datasets
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Statistical comparison of models using grid search
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Compare Stochastic learning strategies for MLPClassifier