:func:`make_blobs` creates a multiclass dataset by allocating each class to one
normally-distributed cluster of points. It provides control over the centers and
standard deviations of each cluster. This dataset is used to demonstrate clustering.

.. plot::
   :context: close-figs
   :scale: 70
   :align: center

   import matplotlib.pyplot as plt
   from sklearn.datasets import make_blobs

   X, y = make_blobs(centers=3, cluster_std=0.5, random_state=0)

   plt.scatter(X[:, 0], X[:, 1], c=y)
   plt.title("Three normally-distributed clusters")
   plt.show()

:func:`make_classification` also creates multiclass datasets but specializes in
introducing noise by way of: correlated, redundant and uninformative features; multiple
Gaussian clusters per class; and linear transformations of the feature space.

.. plot::
   :context: close-figs
   :scale: 70
   :align: center

   import matplotlib.pyplot as plt
   from sklearn.datasets import make_classification

   fig, axs = plt.subplots(1, 3, figsize=(12, 4), sharey=True, sharex=True)
   titles = ["Two classes,\none informative feature,\none cluster per class",
             "Two classes,\ntwo informative features,\ntwo clusters per class",
             "Three classes,\ntwo informative features,\none cluster per class"]
   params = [
       {"n_informative": 1, "n_clusters_per_class": 1, "n_classes": 2},
       {"n_informative": 2, "n_clusters_per_class": 2, "n_classes": 2},
       {"n_informative": 2, "n_clusters_per_class": 1, "n_classes": 3}
   ]

   for i, param in enumerate(params):
       X, Y = make_classification(n_features=2, n_redundant=0, random_state=1, **param)
       axs[i].scatter(X[:, 0], X[:, 1], c=Y)
       axs[i].set_title(titles[i])

   plt.tight_layout()
   plt.show()

:func:`make_gaussian_quantiles` divides a single Gaussian cluster into
near-equal-size classes separated by concentric hyperspheres.

.. plot::
   :context: close-figs
   :scale: 70
   :align: center

   import matplotlib.pyplot as plt
   from sklearn.datasets import make_gaussian_quantiles

   X, Y = make_gaussian_quantiles(n_features=2, n_classes=3, random_state=0)
   plt.scatter(X[:, 0], X[:, 1], c=Y)
   plt.title("Gaussian divided into three quantiles")
   plt.show()

:func:`make_hastie_10_2` generates a similar binary, 10-dimensional problem.

:func:`make_circles` and :func:`make_moons` generate 2D binary classification
datasets that are challenging to certain algorithms (e.g., centroid-based
clustering or linear classification), including optional Gaussian noise.
They are useful for visualization. :func:`make_circles` produces Gaussian data
with a spherical decision boundary for binary classification, while
:func:`make_moons` produces two interleaving half-circles.


.. plot::
   :context: close-figs
   :scale: 70
   :align: center

   import matplotlib.pyplot as plt
   from sklearn.datasets import make_circles, make_moons

   fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(8, 4))

   X, Y = make_circles(noise=0.1, factor=0.3, random_state=0)
   ax1.scatter(X[:, 0], X[:, 1], c=Y)
   ax1.set_title("make_circles")

   X, Y = make_moons(noise=0.1, random_state=0)
   ax2.scatter(X[:, 0], X[:, 1], c=Y)
   ax2.set_title("make_moons")

   plt.tight_layout()
   plt.show()



Multilabel
~~~~~~~~~~

:func:`make_multilabel_classification` generates random samples with multiple
labels, reflecting a bag of words drawn from a mixture of topics. The number of
topics for each document is drawn from a Poisson distribution, and the topics
themselves are drawn from a fixed random distribution. Similarly, the number of
words is drawn from Poisson, with words drawn from a multinomial, where each
topic defines a probability distribution over words. Simplifications with
respect to true bag-of-words mixtures include:

* Per-topic word distributions are independently drawn, where in reality all
  would be affected by a sparse base distribution, and would be correlated.
* For a document generated from multiple topics, all topics are weighted
  equally in generating its bag of words.
* Documents without labels words at random, rather than from a base
  distribution.

.. image:: ../auto_examples/datasets/images/sphx_glr_plot_random_multilabel_dataset_001.png
   :target: ../auto_examples/datasets/plot_random_multilabel_dataset.html
   :scale: 50
   :align: center

Biclustering