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#
![](../../_images/sphx_glr_plot_cluster_comparison_thumb.png)
Comparing different clustering algorithms on toy datasets
![](../../_images/sphx_glr_plot_linkage_comparison_thumb.png)
Comparing different hierarchical linkage methods on toy datasets
![](../../_images/sphx_glr_plot_anomaly_comparison_thumb.png)
Comparing anomaly detection algorithms for outlier detection on toy datasets
![](../../_images/sphx_glr_plot_grid_search_stats_thumb.png)
Statistical comparison of models using grid search
![](../../_images/sphx_glr_plot_mlp_training_curves_thumb.png)
Compare Stochastic learning strategies for MLPClassifier