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.

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.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 0.540 seconds)

Estimated memory usage: 8 MB

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