Pushing the docs to dev/ for branch: main, commit 838ca42a132a286a682b9dc4f730c1da18404d04

Author: sklearn-ci

dev/_downloads/00ae629d652473137a3905a5e08ea815/plot_iris_dtc.py

@@ -66,7 +66,6 @@
             X[idx, 1],
             c=color,
             label=iris.target_names[i],
-            cmap=plt.cm.RdYlBu,
             edgecolor="black",
             s=15,
         )

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -40,7 +40,7 @@
       },
       "outputs": [],
       "source": [
-        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Parameters\nn_classes = 3\nplot_colors = \"ryb\"\nplot_step = 0.02\n\n\nfor pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):\n    # We only take the two corresponding features\n    X = iris.data[:, pair]\n    y = iris.target\n\n    # Train\n    clf = DecisionTreeClassifier().fit(X, y)\n\n    # Plot the decision boundary\n    ax = plt.subplot(2, 3, pairidx + 1)\n    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        cmap=plt.cm.RdYlBu,\n        response_method=\"predict\",\n        ax=ax,\n        xlabel=iris.feature_names[pair[0]],\n        ylabel=iris.feature_names[pair[1]],\n    )\n\n    # Plot the training points\n    for i, color in zip(range(n_classes), plot_colors):\n        idx = np.where(y == i)\n        plt.scatter(\n            X[idx, 0],\n            X[idx, 1],\n            c=color,\n            label=iris.target_names[i],\n            cmap=plt.cm.RdYlBu,\n            edgecolor=\"black\",\n            s=15,\n        )\n\nplt.suptitle(\"Decision surface of decision trees trained on pairs of features\")\nplt.legend(loc=\"lower right\", borderpad=0, handletextpad=0)\n_ = plt.axis(\"tight\")"
+        "import matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.inspection import DecisionBoundaryDisplay\nfrom sklearn.tree import DecisionTreeClassifier\n\n# Parameters\nn_classes = 3\nplot_colors = \"ryb\"\nplot_step = 0.02\n\n\nfor pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2], [1, 3], [2, 3]]):\n    # We only take the two corresponding features\n    X = iris.data[:, pair]\n    y = iris.target\n\n    # Train\n    clf = DecisionTreeClassifier().fit(X, y)\n\n    # Plot the decision boundary\n    ax = plt.subplot(2, 3, pairidx + 1)\n    plt.tight_layout(h_pad=0.5, w_pad=0.5, pad=2.5)\n    DecisionBoundaryDisplay.from_estimator(\n        clf,\n        X,\n        cmap=plt.cm.RdYlBu,\n        response_method=\"predict\",\n        ax=ax,\n        xlabel=iris.feature_names[pair[0]],\n        ylabel=iris.feature_names[pair[1]],\n    )\n\n    # Plot the training points\n    for i, color in zip(range(n_classes), plot_colors):\n        idx = np.where(y == i)\n        plt.scatter(\n            X[idx, 0],\n            X[idx, 1],\n            c=color,\n            label=iris.target_names[i],\n            edgecolor=\"black\",\n            s=15,\n        )\n\nplt.suptitle(\"Decision surface of decision trees trained on pairs of features\")\nplt.legend(loc=\"lower right\", borderpad=0, handletextpad=0)\n_ = plt.axis(\"tight\")"
       ]
     },
     {