.. _sphx_glr_auto_examples_datasets_plot_random_dataset.py: ============================================== Plot randomly generated classification dataset ============================================== Plot several randomly generated 2D classification datasets. This example illustrates the :func:`datasets.make_classification` :func:`datasets.make_blobs` and :func:`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. .. image:: /auto_examples/datasets/images/sphx_glr_plot_random_dataset_001.png :align: center .. code-block:: python 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) 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 0.324 seconds) .. container:: sphx-glr-download **Download Python source code:** :download:`plot_random_dataset.py ` .. container:: sphx-glr-download **Download IPython notebook:** :download:`plot_random_dataset.ipynb `