.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/datasets/plot_random_dataset.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code or to run this example in your browser via JupyterLite or Binder .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_datasets_plot_random_dataset.py: ============================================== Plot randomly generated classification dataset ============================================== This example plots several randomly generated classification datasets. For easy visualization, all datasets have 2 features, plotted on the x and y axis. The color of each point represents its class label. The first 4 plots use the :func:`~sklearn.datasets.make_classification` with different numbers of informative features, clusters per class and classes. The final 2 plots use :func:`~sklearn.datasets.make_blobs` and :func:`~sklearn.datasets.make_gaussian_quantiles`. .. GENERATED FROM PYTHON SOURCE LINES 16-61 .. image-sg:: /auto_examples/datasets/images/sphx_glr_plot_random_dataset_001.png :alt: One informative feature, one cluster per class, Two informative features, one cluster per class, Two informative features, two clusters per class, Multi-class, two informative features, one cluster, Three blobs, Gaussian divided into three quantiles :srcset: /auto_examples/datasets/images/sphx_glr_plot_random_dataset_001.png :class: sphx-glr-single-img .. code-block:: Python import matplotlib.pyplot as plt from sklearn.datasets import make_blobs, make_classification, make_gaussian_quantiles plt.figure(figsize=(8, 8)) plt.subplots_adjust(bottom=0.05, top=0.9, left=0.05, right=0.95) 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() .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 0.289 seconds) .. _sphx_glr_download_auto_examples_datasets_plot_random_dataset.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.4.X?urlpath=lab/tree/notebooks/auto_examples/datasets/plot_random_dataset.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/?path=auto_examples/datasets/plot_random_dataset.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_random_dataset.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_random_dataset.py ` .. include:: plot_random_dataset.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_