Pushing the docs to dev/ for branch: master, commit c77003caf90dd5b0d1b379afa2b08dee27090fbe

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
     }, 
     {
       "source": [
-        "\n# IsolationForest example\n\n\nAn example using IsolationForest for anomaly detection.\n\nThe IsolationForest 'isolates' observations by randomly selecting a feature\nand then randomly selecting a split value between the maximum and minimum\nvalues of the selected feature.\n\nSince recursive partitioning can be represented by a tree structure, the\nnumber of splittings required to isolate a sample is equivalent to the path\nlength from the root node to the terminating node.\n\nThis path length, averaged over a forest of such random trees, is a measure\nof abnormality and our decision function.\n\nRandom partitioning produces noticeable shorter paths for anomalies.\nHence, when a forest of random trees collectively produce shorter path lengths\nfor particular samples, they are highly likely to be anomalies.\n\n.. [1] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. \"Isolation forest.\"\n    Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.\n\n\n"
+        "\n# IsolationForest example\n\n\nAn example using IsolationForest for anomaly detection.\n\nThe IsolationForest 'isolates' observations by randomly selecting a feature\nand then randomly selecting a split value between the maximum and minimum\nvalues of the selected feature.\n\nSince recursive partitioning can be represented by a tree structure, the\nnumber of splittings required to isolate a sample is equivalent to the path\nlength from the root node to the terminating node.\n\nThis path length, averaged over a forest of such random trees, is a measure\nof normality and our decision function.\n\nRandom partitioning produces noticeable shorter paths for anomalies.\nHence, when a forest of random trees collectively produce shorter path lengths\nfor particular samples, they are highly likely to be anomalies.\n\n.. [1] Liu, Fei Tony, Ting, Kai Ming and Zhou, Zhi-Hua. \"Isolation forest.\"\n    Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on.\n\n\n"
       ], 
       "cell_type": "markdown", 
       "metadata": {}

dev/_downloads/auto_examples_python.zip

@@ -14,7 +14,7 @@
 length from the root node to the terminating node.
 
 This path length, averaged over a forest of such random trees, is a measure
-of abnormality and our decision function.
+of normality and our decision function.
 
 Random partitioning produces noticeable shorter paths for anomalies.
 Hence, when a forest of random trees collectively produce shorter path lengths