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

* remove unused condition in decision tree construction

* edit is_leaf condition for min_weight_leaf

* edit ordering of statements

* remove extra parens and add whats new

doc/whats_new.rst

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

sklearn/tree/_tree.pyx

@@ -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()
@@ -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)