8000 [MRG+1] correct condition in decision tree construction by nelson-liu · Pull Request #7441 · scikit-learn/scikit-learn · GitHub
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[MRG+1] correct condition in decision tree construction #7441

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Merged
merged 4 commits into from
Sep 29, 2016
Merged

[MRG+1] correct condition in decision tree construction #7441

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ceaa894
remove unused condition in decision tree construction
nelson-liu Sep 15, 2016
b897dad
edit is_leaf condition for min_weight_leaf
nelson-liu Sep 28, 2016
57a5c44
edit ordering of statements
nelson-liu Sep 28, 2016
35ed851
remove extra parens and add whats new
nelson-liu Sep 28, 2016
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7 changes: 7 additions & 0 deletions doc/whats_new.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -20,6 +20,13 @@ New features
Enhancements
............

- Edited criterion for leaf nodes in decision tree criterion by declaring a
node as a leaf if the weighted number of samples at the node is less than
2 * the minimum weight specified to be at a node. This makes growth more
efficient, but trees using parameters that modify the weight at each leaf
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I think this is a confusing way of stating it. Will fix it up in master.

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thanks @jnothman

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20d0b00

will be grown differently. (`#7441
<https://github.com/scikit-learn/scikit-learn/pull/7441>`_) by `Nelson
Liu`_.

Bug fixes
.........
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18 changes: 9 additions & 9 deletions sklearn/tree/_tree.pyx
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Expand Up @@ -216,10 +216,10 @@ cdef class DepthFirstTreeBuilder(TreeBuilder):
n_node_samples = end - start
splitter.node_reset(start, end, &weighted_n_node_samples)

is_leaf = ((depth >= max_depth) or
(n_node_samples < min_samples_split) or
(n_node_samples < 2 * min_samples_leaf) or
(weighted_n_node_samples < min_weight_leaf))
is_leaf = (depth >= max_depth or
n_node_samples < min_samples_split or
n_node_samples < 2 * min_samples_leaf or
weighted_n_node_samples < 2 * min_weight_leaf)

if first:
impurity = splitter.node_impurity()
Expand Down Expand Up @@ -436,11 +436,11 @@ cdef class BestFirstTreeBuilder(TreeBuilder):
impurity = splitter.node_impurity()

n_node_samples = end - start
is_leaf = ((depth > self.max_depth) or
(n_node_samples < self.min_samples_split) or
(n_node_samples < 2 * self.min_samples_leaf) or
(weighted_n_node_samples < self.min_weight_leaf) or
(impurity <= min_impurity_split))
is_leaf = (depth > self.max_depth or
n_node_samples < self.min_samples_split or
n_node_samples < 2 * self.min_samples_leaf or
weighted_n_node_samples < 2 * self.min_weight_leaf or
impurity <= min_impurity_split)

if not is_leaf:
splitter.node_split(impurity, &split, &n_constant_features)
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