Pushing the docs to dev/ for branch: master, commit 0307afa17953f11b88e6910fcafd9e09f450906e

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Plot the decision boundaries of a VotingClassifier\n\n\nPlot the decision boundaries of a `VotingClassifier` for\ntwo features of the Iris dataset.\n\nPlot the class probabilities of the first sample in a toy dataset\npredicted by three different classifiers and averaged by the\n`VotingClassifier`.\n\nFirst, three exemplary classifiers are initialized (`DecisionTreeClassifier`,\n`KNeighborsClassifier`, and `SVC`) and used to initialize a\nsoft-voting `VotingClassifier` with weights `[2, 1, 2]`, which means that\nthe predicted probabilities of the `DecisionTreeClassifier` and `SVC`\ncount 5 times as much as the weights of the `KNeighborsClassifier` classifier\nwhen the averaged probability is calculated.\n"
+        "\n# Plot the decision boundaries of a VotingClassifier\n\n\nPlot the decision boundaries of a `VotingClassifier` for\ntwo features of the Iris dataset.\n\nPlot the class probabilities of the first sample in a toy dataset\npredicted by three different classifiers and averaged by the\n`VotingClassifier`.\n\nFirst, three exemplary classifiers are initialized (`DecisionTreeClassifier`,\n`KNeighborsClassifier`, and `SVC`) and used to initialize a\nsoft-voting `VotingClassifier` with weights `[2, 1, 2]`, which means that\nthe predicted probabilities of the `DecisionTreeClassifier` and `SVC`\neach count 2 times as much as the weights of the `KNeighborsClassifier`\nclassifier when the averaged probability is calculated.\n"
       ]
     },
     {

dev/_downloads/d34667f097c619f8afda4bc936e7af21/auto_examples_jupyter.zip

@@ -14,8 +14,8 @@
 `KNeighborsClassifier`, and `SVC`) and used to initialize a
 soft-voting `VotingClassifier` with weights `[2, 1, 2]`, which means that
 the predicted probabilities of the `DecisionTreeClassifier` and `SVC`
-count 5 times as much as the weights of the `KNeighborsClassifier` classifier
-when the averaged probability is calculated.
+each count 2 times as much as the weights of the `KNeighborsClassifier`
+classifier when the averaged probability is calculated.
 
 """
 print(__doc__)