Pushing the docs to dev/ for branch: main, commit 9670b4243dce06255b341cb3aa613c0da0aeb212

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: b4b9d4a66b614a428ea350785a47981b
+config: ba7e766cb95b85ba3a1f6ff755258a3f
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/06ffeb4f0ded6447302acd5a712f8490/plot_nearest_centroid.ipynb

@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nfrom sklearn import datasets\nfrom sklearn.neighbors import NearestCentroid\nfrom sklearn.inspection import DecisionBoundaryDisplay\n\nn_neighbors = 15\n\n# import some data to play with\niris = datasets.load_iris()\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 = ListedColormap([\"darkorange\", \"c\", \"darkblue\"])\n\nfor shrinkage in [None, 0.2]:\n    # we create an instance of Neighbours Classifier and fit the data.\n    clf = NearestCentroid(shrink_threshold=shrinkage)\n    clf.fit(X, y)\n    y_pred = clf.predict(X)\n    print(shrinkage, np.mean(y == y_pred))\n\n    _, ax = plt.subplots()\n    DecisionBoundaryDisplay.from_estimator(\n        clf, X, cmap=cmap_light, ax=ax, response_method=\"predict\"\n    )\n\n    # Plot also the training points\n    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor=\"k\", s=20)\n    plt.title(\"3-Class classification (shrink_threshold=%r)\" % shrinkage)\n    plt.axis(\"tight\")\n\nplt.show()"
+        "import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nfrom sklearn import datasets\nfrom sklearn.neighbors import NearestCentroid\nfrom sklearn.inspection import DecisionBoundaryDisplay\n\n\n# import some data to play with\niris = datasets.load_iris()\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 = ListedColormap([\"darkorange\", \"c\", \"darkblue\"])\n\nfor shrinkage in [None, 0.2]:\n    # we create an instance of Nearest Centroid Classifier and fit the data.\n    clf = NearestCentroid(shrink_threshold=shrinkage)\n    clf.fit(X, y)\n    y_pred = clf.predict(X)\n    print(shrinkage, np.mean(y == y_pred))\n\n    _, ax = plt.subplots()\n    DecisionBoundaryDisplay.from_estimator(\n        clf, X, cmap=cmap_light, ax=ax, response_method=\"predict\"\n    )\n\n    # Plot also the training points\n    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold, edgecolor=\"k\", s=20)\n    plt.title(\"3-Class classification (shrink_threshold=%r)\" % shrinkage)\n    plt.axis(\"tight\")\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -15,7 +15,6 @@
 from sklearn.neighbors import NearestCentroid
 from sklearn.inspection import DecisionBoundaryDisplay
 
-n_neighbors = 15
 
 # import some data to play with
 iris = datasets.load_iris()
@@ -29,7 +28,7 @@
 cmap_bold = ListedColormap(["darkorange", "c", "darkblue"])
 
 for shrinkage in [None, 0.2]:
-    # we create an instance of Neighbours Classifier and fit the data.
+    # we create an instance of Nearest Centroid Classifier and fit the data.
     clf = NearestCentroid(shrink_threshold=shrinkage)
     clf.fit(X, y)
     y_pred = clf.predict(X)