This is documentation for an old release of Scikit-learn (version 0.21). Try the latest stable release (version 1.6) or development (unstable) versions.

This is documentation for an old release of Scikit-learn (version 0.21). Try the latest stable release (version 1.6) or development (unstable) versions.

Plot randomly generated classification dataset

Plot several randomly generated 2D classification datasets. This example illustrates the datasets.make_classification datasets.make_blobs and datasets.make_gaussian_quantiles functions.

For make_classification, three binary and two multi-class classification datasets are generated, with different numbers of informative features and clusters per class.

../../_images/sphx_glr_plot_random_dataset_001.png
print(__doc__)

import matplotlib.pyplot as plt

from sklearn.datasets import make_classification
from sklearn.datasets import make_blobs
from sklearn.datasets import make_gaussian_quantiles

plt.figure(figsize=(8, 8))
plt.subplots_adjust(bottom=.05, top=.9, left=.05, right=.95)

plt.subplot(321)
plt.title("One informative feature, one cluster per class", fontsize='small')
X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=1,
                             n_clusters_per_class=1)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,
            s=25, edgecolor='k')

plt.subplot(322)
plt.title("Two informative features, one cluster per class", fontsize='small')
X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,
                             n_clusters_per_class=1)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,
            s=25, edgecolor='k')

plt.subplot(323)
plt.title("Two informative features, two clusters per class",
          fontsize='small')
X2, Y2 = make_classification(n_features=2, n_redundant=0, n_informative=2)
plt.scatter(X2[:, 0], X2[:, 1], marker='o', c=Y2,
            s=25, edgecolor='k')

plt.subplot(324)
plt.title("Multi-class, two informative features, one cluster",
          fontsize='small')
X1, Y1 = make_classification(n_features=2, n_redundant=0, n_informative=2,
                             n_clusters_per_class=1, n_classes=3)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,
            s=25, edgecolor='k')

plt.subplot(325)
plt.title("Three blobs", fontsize='small')
X1, Y1 = make_blobs(n_features=2, centers=3)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,
            s=25, edgecolor='k')

plt.subplot(326)
plt.title("Gaussian divided into three quantiles", fontsize='small')
X1, Y1 = make_gaussian_quantiles(n_features=2, n_classes=3)
plt.scatter(X1[:, 0], X1[:, 1], marker='o', c=Y1,
            s=25, edgecolor='k')

plt.show()

Total running time of the script: ( 0 minutes 0.097 seconds)

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