Pushing the docs for revision for branch: master, commit 5bb983013c71d8e9bcd7bed8654e60b1bcf20a47

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
     }, 
     {
       "source": [
-        "\n# Comparing random forests and the multi-output meta estimator\n\n\nAn example to compare multi-output regression with random forest and\nthe `multioutput.MultiOutputRegressor <_multiclass>` meta-estimator.\n\nThis example illustrates the use of the\n`multioutput.MultiOutputRegressor <_multiclass>` meta-estimator\nto perform multi-output regression. A random forest regressor is used,\nwhich supports multi-output regression natively, so the results can be\ncompared.\n\nThe random forest regressor will only ever predict values within the\nrange of observations or closer to zero for each of the targets. As a\nresult the predictions are biased towards the centre of the circle.\n\nUsing a single underlying feature the model learns both the\nx and y coordinate as output.\n\n\n"
+        "\n# Comparing random forests and the multi-output meta estimator\n\n\nAn example to compare multi-output regression with random forest and\nthe `multioutput.MultiOutputRegressor <multiclass>` meta-estimator.\n\nThis example illustrates the use of the\n`multioutput.MultiOutputRegressor <multiclass>` meta-estimator\nto perform multi-output regression. A random forest regressor is used,\nwhich supports multi-output regression natively, so the results can be\ncompared.\n\nThe random forest regressor will only ever predict values within the\nrange of observations or closer to zero for each of the targets. As a\nresult the predictions are biased towards the centre of the circle.\n\nUsing a single underlying feature the model learns both the\nx and y coordinate as output.\n\n\n"
       ], 
       "cell_type": "markdown", 
       "metadata": {}

dev/_downloads/auto_examples_python.zip

@@ -4,10 +4,10 @@
 ============================================================
 
 An example to compare multi-output regression with random forest and
-the :ref:`multioutput.MultiOutputRegressor <_multiclass>` meta-estimator.
+the :ref:`multioutput.MultiOutputRegressor <multiclass>` meta-estimator.
 
 This example illustrates the use of the
-:ref:`multioutput.MultiOutputRegressor <_multiclass>` meta-estimator
+:ref:`multioutput.MultiOutputRegressor <multiclass>` meta-estimator
 to perform multi-output regression. A random forest regressor is used,
 which supports multi-output regression natively, so the results can be
 compared.