Pushing the docs to dev/ for branch: main, commit 0238d1f1076ec849c2836faf23cebafc3f2f9e7a

Author: sklearn-ci

dev/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 0a7af102b32f002f14fad512c871e2cb
+config: bd9c0ef975f43dabbc9cb1bad322c12a
 tags: 645f666f9bcd5a90fca523b33c5a78b7

dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip

@@ -68,7 +68,7 @@
 #   - ``weighted_n_node_samples[i]``: the weighted number of training samples
 #     reaching node ``i``
 #   - ``value[i, j, k]``: the summary of the training samples that reached node i for
-#     class j and output k.
+#     output j and class k (for regression tree, class is set to 1).
 #
 # Using the arrays, we can traverse the tree structure to compute various
 # properties. Below, we will compute the depth of each node and whether or not

dev/_downloads/21a6ff17ef2837fe1cd49e63223a368d/plot_unveil_tree_structure.py

@@ -40,7 +40,7 @@
       "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. The\n``tree_.compute_node_depths()`` method computes the depth of each node in the\ntree. `tree_` also stores the entire binary tree structure, represented as a\nnumber of parallel arrays. The i-th element of each array holds information\nabout the node ``i``. Node 0 is the tree's root. Some of the arrays only\napply to either leaves or split nodes. In this case the values of the nodes\nof the other type is arbitrary. For example, the arrays ``feature`` and\n``threshold`` only apply to split nodes. The values for leaf nodes in these\narrays 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  - ``weighted_n_node_samples[i]``: the weighted number of training samples\n    reaching node ``i``\n  - ``value[i, j, k]``: the summary of the training samples that reached node i for\n    class j and output k.\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. The\n``tree_.compute_node_depths()`` method computes the depth of each node in the\ntree. `tree_` also stores the entire binary tree structure, represented as a\nnumber of parallel arrays. The i-th element of each array holds information\nabout the node ``i``. Node 0 is the tree's root. Some of the arrays only\napply to either leaves or split nodes. In this case the values of the nodes\nof the other type is arbitrary. For example, the arrays ``feature`` and\n``threshold`` only apply to split nodes. The values for leaf nodes in these\narrays 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  - ``weighted_n_node_samples[i]``: the weighted number of training samples\n    reaching node ``i``\n  - ``value[i, j, k]``: the summary of the training samples that reached node i for\n    output j and class k (for regression tree, class is set to 1).\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"
       ]
     },
     {