sklearn DecisionTree tree_

Sklearn API -
Understanding the decision tree structure

Array-based representation of a binary decision tree.
The binary tree is represented as a number of parallel arrays. The i-th
element of each array holds information about the node i. Node 0 is the tree’s root. You can find a detailed description of all arrays in _tree.pxd. NOTE: Some of the arrays only apply to either leaves or split nodes, resp. In this case the values of nodes of the other type are arbitrary!

Attributes

• node_count : int
  The number of nodes (internal nodes + leaves) in the tree.
  總節點數（葉節點+內部結點+根節點）

• capacity : int
  The current capacity (i.e., size) of the arrays, which is at least as
  great as node_count.

• max_depth : int
  The depth of the tree, i.e. the maximum depth of its leaves.
  樹的深度

• children_left : array of int, shape [node_count]
  children_left[i] holds the node id of the left child of node i.
  For leaves, children_left[i] == TREE_LEAF. Otherwise,
  children_left[i] > i. This child handles the case where
  X[:, feature[i]] <= threshold[i].
  Note:TREE_LEAF = -1
  the “children_left” array mean

• children_right : array of int, shape [node_count]
  children_right[i] holds the node id of the right child of node i.
  For leaves, children_right[i] == TREE_LEAF. Otherwise,
  children_right[i] > i. This child handles the case where
  X[:, feature[i]] > threshold[i].

• feature : array of int, shape [node_count]
  feature[i] holds the feature to split on, for the internal node i.
  第i個節點（內部結點）的分割特徵

• threshold : array of double, shape [node_count]
  threshold[i] holds the threshold for the internal node i.
  結合feature,第i個節點分割特徵的閾值，eg,小於該閾值歸位左分支，大於該閾值，歸位右分支。

• value : array of double, shape [node_count, n_outputs, max_n_classes]
  Contains the constant prediction value of each node.

• impurity : array of double, shape [node_count]
  impurity[i] holds the impurity (i.e., the value of the splitting
  criterion) at node i.

• n_node_samples : array of int, shape [node_count]
  n_node_samples[i] holds the number of training samples reaching node i.

• weighted_n_node_samples : array of int, shape [node_count]
  weighted_n_node_samples[i] holds the weighted number of training samples reaching node i.

後期會整理出用於中文文本分類的決策樹的節點刪除（人爲後剪枝）和替換，有助於利用決策樹提取規則，現在大家可以先參考此鏈接How to extract the decision rules from scikit-learn decision-tree?
。