Pushing the docs to dev/ for branch: main, commit 0b978cb86f2fd23bef9940cf04254cbd05d2a661

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -249,7 +249,7 @@
       "cell_type": "markdown",
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-        "## One-vs-One multiclass ROC\n\nThe One-vs-One (OvO) multiclass strategy consists in fitting one classifier\nper class pair. Since it requires to train `n_classes` * (`n_classes` - 1) / 2\nclassifiers, this method is usually slower than One-vs-Rest due to its\nO(`n_classes`^2) complexity.\n\nIn this section, we demonstrate the macro-averaged AUC using the OvO scheme\nfor the 3 possible combinations in the `iris_dataset`: \"setosa\" vs\n\"versicolor\", \"versicolor\" vs \"virginica\" and  \"virginica\" vs \"setosa\". Notice\nthat micro-averaging is not defined for the OvO scheme.\n\n### ROC curve using the OvO macro-average\n\nIn the OvO scheme, the first step is to identify all possible unique\ncombinations of pairs. The computation of scores is done by treating one of\nthe elements in a given pair as the positive class and the other element as\nthe negative class, then re-computing the score by inversing the roles and\ntaking the mean of both scores.\n\n"
+        "## One-vs-One multiclass ROC\n\nThe One-vs-One (OvO) multiclass strategy consists in fitting one classifier\nper class pair. Since it requires to train `n_classes` * (`n_classes` - 1) / 2\nclassifiers, this method is usually slower than One-vs-Rest due to its\nO(`n_classes` ^2) complexity.\n\nIn this section, we demonstrate the macro-averaged AUC using the OvO scheme\nfor the 3 possible combinations in the `iris_dataset`: \"setosa\" vs\n\"versicolor\", \"versicolor\" vs \"virginica\" and  \"virginica\" vs \"setosa\". Notice\nthat micro-averaging is not defined for the OvO scheme.\n\n### ROC curve using the OvO macro-average\n\nIn the OvO scheme, the first step is to identify all possible unique\ncombinations of pairs. The computation of scores is done by treating one of\nthe elements in a given pair as the positive class and the other element as\nthe negative class, then re-computing the score by inversing the roles and\ntaking the mean of both scores.\n\n"
       ]
     },
     {