1.5/notebooks/auto_examples/datasets/plot_iris_dataset.ipynb

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        "\n# The Iris Dataset\nThis data sets consists of 3 different types of irises'\n(Setosa, Versicolour, and Virginica) petal and sepal\nlength, stored in a 150x4 numpy.ndarray\n\nThe rows being the samples and the columns being:\nSepal Length, Sepal Width, Petal Length and Petal Width.\n\nThe below plot uses the first two features.\nSee [here](https://en.wikipedia.org/wiki/Iris_flower_data_set) for more\ninformation on this dataset.\n"
      ]
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      "source": [
        "# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Loading the iris dataset\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "from sklearn import datasets\n\niris = datasets.load_iris()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Scatter Plot of the Iris dataset\n\n"
      ]
    },
    {
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      "execution_count": null,
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        "import matplotlib.pyplot as plt\n\n_, ax = plt.subplots()\nscatter = ax.scatter(iris.data[:, 0], iris.data[:, 1], c=iris.target)\nax.set(xlabel=iris.feature_names[0], ylabel=iris.feature_names[1])\n_ = ax.legend(\n    scatter.legend_elements()[0], iris.target_names, loc=\"lower right\", title=\"Classes\"\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Each point in the scatter plot refers to one of the 150 iris flowers\nin the dataset, with the color indicating their respective type\n(Setosa, Versicolour, and Virginica).\nYou can already see a pattern regarding the Setosa type, which is\neasily identifiable based on its short and wide sepal. Only\nconsidering these 2 dimensions, sepal width and length, there's still\noverlap between the Versicolor and Virginica types.\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Plot a PCA representation\nLet's apply a Principal Component Analysis (PCA) to the iris dataset\nand then plot the irises across the first three PCA dimensions.\nThis will allow us to better differentiate between the three types!\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "# unused but required import for doing 3d projections with matplotlib < 3.2\nimport mpl_toolkits.mplot3d  # noqa: F401\n\nfrom sklearn.decomposition import PCA\n\nfig = plt.figure(1, figsize=(8, 6))\nax = fig.add_subplot(111, projection=\"3d\", elev=-150, azim=110)\n\nX_reduced = PCA(n_components=3).fit_transform(iris.data)\nax.scatter(\n    X_reduced[:, 0],\n    X_reduced[:, 1],\n    X_reduced[:, 2],\n    c=iris.target,\n    s=40,\n)\n\nax.set_title(\"First three PCA dimensions\")\nax.set_xlabel(\"1st Eigenvector\")\nax.xaxis.set_ticklabels([])\nax.set_ylabel(\"2nd Eigenvector\")\nax.yaxis.set_ticklabels([])\nax.set_zlabel(\"3rd Eigenvector\")\nax.zaxis.set_ticklabels([])\n\nplt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "PCA will create 3 new features that are a linear combination of the\n4 original features. In addition, this transform maximizes the variance.\nWith this transformation, we see that we can identify each species using\nonly the first feature (i.e. first eigenvalues).\n\n"
      ]
    }
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