CODEWITHSUNDEEP
pythonmachine-learningscikit-learndecision-tree
Coder
Coder

Reputation: 607

Getting the value of a leaf node in a DecisionTreeRegressor

I've been trying to analyze the DecisionTreeRegressor I trained in sklearn. I found http://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html useful in determining the attributes that split each branch in the tree, specifically this code snippet:

n_nodes = estimator.tree_.node_count
children_left = estimator.tree_.children_left
children_right = estimator.tree_.children_right
feature = estimator.tree_.feature
threshold = estimator.tree_.threshold

# The tree structure can be traversed to compute various properties such
# as the depth of each node and whether or not it is a leaf.
node_depth = np.zeros(shape=n_nodes, dtype=np.int64)
is_leaves = np.zeros(shape=n_nodes, dtype=bool)
stack = [(0, -1)]  # seed is the root node id and its parent depth
while len(stack) > 0:
    node_id, parent_depth = stack.pop()
    node_depth[node_id] = parent_depth + 1

    # If we have a test node
    if (children_left[node_id] != children_right[node_id]):
        stack.append((children_left[node_id], parent_depth + 1))
        stack.append((children_right[node_id], parent_depth + 1))
    else:
        is_leaves[node_id] = True


print("The binary tree structure has %s nodes and has "
      "the following tree structure:"
      % n_nodes)
for i in range(n_nodes):
    if is_leaves[i]:
        print("%snode=%s leaf node." % (node_depth[i] * "\t", i))
    else:
        print("%snode=%s test node: go to node %s if X[:, %s] <= %s else to "
              "node %s."
              % (node_depth[i] * "\t",
                 i,
                 children_left[i],
                 feature[i],
                 threshold[i],
                 children_right[i],
                 ))

However, this doesn't tell me the value of each leaf node. If the above prints out something that looks like this:

The binary tree structure has 7 nodes and has the following tree structure:
node=0 test node: go to node 1 if X[:, 2] <= 1.00764083862 else to node 4.
    node=1 test node: go to node 2 if X[:, 2] <= 0.974808812141 else to node 3.
        node=2 leaf node.
        node=3 leaf node.
    node=4 test node: go to node 5 if X[:, 0] <= -2.90554761887 else to node 6.
        node=5 leaf node.
        node=6 leaf node.

How do I know the value that node 2 represents for example?

Upvotes: 1

Views: 3490

Answers (1)

desertnaut
desertnaut

Reputation: 60319

The method you are looking for is estimator.tree_.value

Let's make a reproducible example, since the one you link to from the docs is for classification and not for regression:

import numpy as np
from sklearn.tree import DecisionTreeRegressor

# dummy data
rng = np.random.RandomState(1)
X = np.sort(5 * rng.rand(80, 1), axis=0)
y = np.sin(X).ravel()
y[::5] += 3 * (0.5 - rng.rand(16))

estimator = DecisionTreeRegressor(max_depth=3)
estimator.fit(X, y)

After that, using your code verbatim, we get:

The binary tree structure has 15 nodes and has the following tree structure: 
node=0 test node: go to node 1 if X[:, 0] <= 3.13275051117 else to node 8. 
       node=1 test node: go to node 2 if X[:, 0] <= 0.513901114464 else to node 5. 
              node=2 test node: go to node 3 if X[:, 0] <= 0.0460066311061 else to node 4. 
                     node=3 leaf node. 
                     node=4 leaf node. 
              node=5 test node: go to node 6 if X[:, 0] <= 2.02933192253 else to node 7. 
                     node=6 leaf node. 
                     node=7 leaf node. 
       node=8 test node: go to node 9 if X[:, 0] <= 3.85022854805 else to node 12. 
              node=9 test node: go to node 10 if X[:, 0] <= 3.42930102348 else to node 11. 
                     node=10 leaf node. 
                     node=11 leaf node. 
              node=12 test node: go to node 13 if X[:, 0] <= 4.68025827408 else to node 14. 
                     node=13 leaf node. 
                     node=14 leaf node.

Now, estimator.tree_.value contains the values for all the tree nodes (here 15):

len(estimator.tree_.value)
# 15

and to get, for example, the value for node #3, we ask

estimator.tree_.value[3]
# array([[-1.1493464]])

For a detailed explanation of the value contents (including non-terminal nodes), see my answers in

  1. interpreting Graphviz output for decision tree regression (for regression) and

  2. What does scikit-learn DecisionTreeClassifier.tree_.value do? (for classification).

Upvotes: 5

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