Plot randomly generated classification dataset — scikit-learn 0.20.4 documentation (original) (raw)
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Plot several randomly generated 2D classification datasets. This example illustrates the datasets.make_classification datasets.make_blobs and datasets.make_gaussian_quantilesfunctions.
For make_classification, three binary and two multi-class classification datasets are generated, with different numbers of informative features and clusters per class.
Out:
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 0.355 seconds)