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    {
      "cell_type": "code",
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      "source": [
        "%matplotlib inline"
      ]
    },
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      "source": [
        "\n# Plot randomly generated classification dataset\n\nThis example plots several randomly generated classification datasets.\nFor easy visualization, all datasets have 2 features, plotted on the x and y\naxis. The color of each point represents its class label.\n\nThe first 4 plots use the :func:`~sklearn.datasets.make_classification` with\ndifferent numbers of informative features, clusters per class and classes.\nThe final 2 plots use :func:`~sklearn.datasets.make_blobs` and\n:func:`~sklearn.datasets.make_gaussian_quantiles`.\n"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      },
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      "source": [
        "import matplotlib.pyplot as plt\n\nfrom sklearn.datasets import make_classification\nfrom sklearn.datasets import make_blobs\nfrom sklearn.datasets import make_gaussian_quantiles\n\nplt.figure(figsize=(8, 8))\nplt.subplots_adjust(bottom=0.05, top=0.9, left=0.05, right=0.95)\n\nplt.subplot(321)\nplt.title(\"One informative feature, one cluster per class\", fontsize=\"small\")\nX1, Y1 = make_classification(\n    n_features=2, n_redundant=0, n_informative=1, n_clusters_per_class=1\n)\nplt.scatter(X1[:, 0], X1[:, 1], marker=\"o\", c=Y1, s=25, edgecolor=\"k\")\n\nplt.subplot(322)\nplt.title(\"Two informative features, one cluster per class\", fontsize=\"small\")\nX1, Y1 = make_classification(\n    n_features=2, n_redundant=0, n_informative=2, n_clusters_per_class=1\n)\nplt.scatter(X1[:, 0], X1[:, 1], marker=\"o\", c=Y1, s=25, edgecolor=\"k\")\n\nplt.subplot(323)\nplt.title(\"Two informative features, two clusters per class\", fontsize=\"small\")\nX2, Y2 = make_classification(n_features=2, n_redundant=0, n_informative=2)\nplt.scatter(X2[:, 0], X2[:, 1], marker=\"o\", c=Y2, s=25, edgecolor=\"k\")\n\nplt.subplot(324)\nplt.title(\"Multi-class, two informative features, one cluster\", fontsize=\"small\")\nX1, Y1 = make_classification(\n    n_features=2, n_redundant=0, n_informative=2, n_clusters_per_class=1, n_classes=3\n)\nplt.scatter(X1[:, 0], X1[:, 1], marker=\"o\", c=Y1, s=25, edgecolor=\"k\")\n\nplt.subplot(325)\nplt.title(\"Three blobs\", fontsize=\"small\")\nX1, Y1 = make_blobs(n_features=2, centers=3)\nplt.scatter(X1[:, 0], X1[:, 1], marker=\"o\", c=Y1, s=25, edgecolor=\"k\")\n\nplt.subplot(326)\nplt.title(\"Gaussian divided into three quantiles\", fontsize=\"small\")\nX1, Y1 = make_gaussian_quantiles(n_features=2, n_classes=3)\nplt.scatter(X1[:, 0], X1[:, 1], marker=\"o\", c=Y1, s=25, edgecolor=\"k\")\n\nplt.show()"
      ]
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