Pushing the docs to dev/ for branch: main, commit 2e3abc2e32eefbfea78f15bcc767ca9bb4911568

Author: sklearn-ci

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -60,7 +60,7 @@
 #     node
 #   - ``feature[i]``: feature used for splitting node ``i``
 #   - ``threshold[i]``: threshold value at node ``i``
-#   - ``n_node_samples[i]``: the number of of training samples reaching node
+#   - ``n_node_samples[i]``: the number of training samples reaching node
 #     ``i``
 #   - ``impurity[i]``: the impurity at node ``i``
 #

dev/_downloads/21a6ff17ef2837fe1cd49e63223a368d/plot_unveil_tree_structure.py

@@ -51,7 +51,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Tree structure\n\nThe decision classifier has an attribute called ``tree_`` which allows access\nto low level attributes such as ``node_count``, the total number of nodes,\nand ``max_depth``, the maximal depth of the tree. It also stores the\nentire binary tree structure, represented as a number of parallel arrays. The\ni-th element of each array holds information about the node ``i``. Node 0 is\nthe tree's root. Some of the arrays only apply to either leaves or split\nnodes. In this case the values of the nodes of the other type is arbitrary.\nFor example, the arrays ``feature`` and ``threshold`` only apply to split\nnodes. The values for leaf nodes in these arrays are therefore arbitrary.\n\nAmong these arrays, we have:\n\n  - ``children_left[i]``: id of the left child of node ``i`` or -1 if leaf\n    node\n  - ``children_right[i]``: id of the right child of node ``i`` or -1 if leaf\n    node\n  - ``feature[i]``: feature used for splitting node ``i``\n  - ``threshold[i]``: threshold value at node ``i``\n  - ``n_node_samples[i]``: the number of of training samples reaching node\n    ``i``\n  - ``impurity[i]``: the impurity at node ``i``\n\nUsing the arrays, we can traverse the tree structure to compute various\nproperties. Below, we will compute the depth of each node and whether or not\nit is a leaf.\n\n"
+        "## Tree structure\n\nThe decision classifier has an attribute called ``tree_`` which allows access\nto low level attributes such as ``node_count``, the total number of nodes,\nand ``max_depth``, the maximal depth of the tree. It also stores the\nentire binary tree structure, represented as a number of parallel arrays. The\ni-th element of each array holds information about the node ``i``. Node 0 is\nthe tree's root. Some of the arrays only apply to either leaves or split\nnodes. In this case the values of the nodes of the other type is arbitrary.\nFor example, the arrays ``feature`` and ``threshold`` only apply to split\nnodes. The values for leaf nodes in these arrays are therefore arbitrary.\n\nAmong these arrays, we have:\n\n  - ``children_left[i]``: id of the left child of node ``i`` or -1 if leaf\n    node\n  - ``children_right[i]``: id of the right child of node ``i`` or -1 if leaf\n    node\n  - ``feature[i]``: feature used for splitting node ``i``\n  - ``threshold[i]``: threshold value at node ``i``\n  - ``n_node_samples[i]``: the number of training samples reaching node\n    ``i``\n  - ``impurity[i]``: the impurity at node ``i``\n\nUsing the arrays, we can traverse the tree structure to compute various\nproperties. Below, we will compute the depth of each node and whether or not\nit is a leaf.\n\n"
       ]
     },
     {