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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 | print(__doc__)import matplotlib.pyplot as pltfrom sklearn.datasets import make_classificationfrom sklearn.datasets import make_blobsfrom sklearn.datasets import make_gaussian_quantilesplt.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)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)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)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)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)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)plt.show() |
Total running time of the script: (0 minutes 0.319 seconds)
Download Python source code:
plot_random_dataset.py
Download IPython notebook:
plot_random_dataset.ipynb
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