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      "source": [
        "\n# Nearest Neighbors Classification\n\nSample usage of Nearest Neighbors classification.\nIt will plot the decision boundaries for each class.\n"
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      "source": [
        "import matplotlib.pyplot as plt\nimport seaborn as sns\nfrom matplotlib.colors import ListedColormap\nfrom sklearn import neighbors, datasets\nfrom sklearn.inspection import DecisionBoundaryDisplay\n\nn_neighbors = 15\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could avoid this ugly\n# slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\n\n# Create color maps\ncmap_light = ListedColormap([\"orange\", \"cyan\", \"cornflowerblue\"])\ncmap_bold = [\"darkorange\", \"c\", \"darkblue\"]\n\nfor weights in [\"uniform\", \"distance\"]:\n    # we create an instance of Neighbours Classifier and fit the data.\n    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n    clf.fit(X, y)\n\n    _, ax = plt.subplots()\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        cmap=cmap_light,\n        ax=ax,\n        response_method=\"predict\",\n        plot_method=\"pcolormesh\",\n        xlabel=iris.feature_names[0],\n        ylabel=iris.feature_names[1],\n        shading=\"auto\",\n    )\n\n    # Plot also the training points\n    sns.scatterplot(\n        x=X[:, 0],\n        y=X[:, 1],\n        hue=iris.target_names[y],\n        palette=cmap_bold,\n        alpha=1.0,\n        edgecolor=\"black\",\n    )\n    plt.title(\n        \"3-Class classification (k = %i, weights = '%s')\" % (n_neighbors, weights)\n    )\n\nplt.show()"
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