Pushing the docs to dev/ for branch: master, commit 2a1d8ba8f92d24b92b186e4b28b3dbf73a4dc0d2

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n# Classifier Chain\n\nExample of using classifier chain on a multilabel dataset.\n\nFor this example we will use the `yeast\nhttp://mldata.org/repository/data/viewslug/yeast/`_ dataset which\ncontains 2417 datapoints each with 103 features and 14 possible labels. Each\ndatapoint has at least one label. As a baseline we first train a logistic\nregression classifier for each of the 14 labels. To evaluate the performance\nof these classifiers we predict on a held-out test set and calculate the\n`User Guide <jaccard_similarity_score>`.\n\nNext we create 10 classifier chains. Each classifier chain contains a\nlogistic regression model for each of the 14 labels. The models in each\nchain are ordered randomly. In addition to the 103 features in the dataset,\neach model gets the predictions of the preceding models in the chain as\nfeatures (note that by default at training time each model gets the true\nlabels as features). These additional features allow each chain to exploit\ncorrelations among the classes. The Jaccard similarity score for each chain\ntends to be greater than that of the set independent logistic models.\n\nBecause the models in each chain are arranged randomly there is significant\nvariation in performance among the chains. Presumably there is an optimal\nordering of the classes in a chain that will yield the best performance.\nHowever we do not know that ordering a priori. Instead we can construct an\nvoting ensemble of classifier chains by averaging the binary predictions of\nthe chains and apply a threshold of 0.5. The Jaccard similarity score of the\nensemble is greater than that of the independent models and tends to exceed\nthe score of each chain in the ensemble (although this is not guaranteed\nwith randomly ordered chains).\n\n"
+        "\n# Classifier Chain\n\nExample of using classifier chain on a multilabel dataset.\n\nFor this example we will use the `yeast\n<http://mldata.org/repository/data/viewslug/yeast>`_ dataset which\ncontains 2417 datapoints each with 103 features and 14 possible labels. Each\ndatapoint has at least one label. As a baseline we first train a logistic\nregression classifier for each of the 14 labels. To evaluate the performance\nof these classifiers we predict on a held-out test set and calculate the\n`User Guide <jaccard_similarity_score>`.\n\nNext we create 10 classifier chains. Each classifier chain contains a\nlogistic regression model for each of the 14 labels. The models in each\nchain are ordered randomly. In addition to the 103 features in the dataset,\neach model gets the predictions of the preceding models in the chain as\nfeatures (note that by default at training time each model gets the true\nlabels as features). These additional features allow each chain to exploit\ncorrelations among the classes. The Jaccard similarity score for each chain\ntends to be greater than that of the set independent logistic models.\n\nBecause the models in each chain are arranged randomly there is significant\nvariation in performance among the chains. Presumably there is an optimal\nordering of the classes in a chain that will yield the best performance.\nHowever we do not know that ordering a priori. Instead we can construct an\nvoting ensemble of classifier chains by averaging the binary predictions of\nthe chains and apply a threshold of 0.5. The Jaccard similarity score of the\nensemble is greater than that of the independent models and tends to exceed\nthe score of each chain in the ensemble (although this is not guaranteed\nwith randomly ordered chains).\n\n"
       ]
     },
     {

dev/_downloads/auto_examples_python.zip

@@ -5,7 +5,7 @@
 Example of using classifier chain on a multilabel dataset.
 
 For this example we will use the `yeast
-http://mldata.org/repository/data/viewslug/yeast/`_ dataset which
+<http://mldata.org/repository/data/viewslug/yeast>`_ dataset which
 contains 2417 datapoints each with 103 features and 14 possible labels. Each
 datapoint has at least one label. As a baseline we first train a logistic
 regression classifier for each of the 14 labels. To evaluate the performance