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sklearn/ensemble/_hist_gradient_boosting/grower.py
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...emble/_hist_gradient_boosting/gradient_boosting.py
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105  105  other_node : TreeNode 

106  106  The node to compare with. 

107  107  """ 

108    if self.split_info is None or other_node.split_info is None: 

109    raise ValueError("Cannot compare nodes without split_info") 

110  108  return self.split_info.gain > other_node.split_info.gain 

111  109  
112  110 
212  210  raise ValueError( 

213  211  "X_binned should be passed as Fortran contiguous " 

214  212  "array for maximum efficiency.") 

215    if max_leaf_nodes is not None and max_leaf_nodes < 1: 

213  +  if max_leaf_nodes is not None and max_leaf_nodes <= 1: 

216  214  raise ValueError('max_leaf_nodes={} should not be' 

217    ' smaller than 1'.format(max_leaf_nodes)) 

218    if max_depth is not None and max_depth < 1: 

215  +  ' smaller than 2'.format(max_leaf_nodes)) 

216  +  if max_depth is not None and max_depth <= 1: 

219  217  raise ValueError('max_depth={} should not be' 

220    ' smaller than 1'.format(max_depth)) 

218  +  ' smaller than 2'.format(max_depth)) 

221  219  if min_samples_leaf < 1: 

222  220  raise ValueError('min_samples_leaf={} should ' 

223  221  'not be smaller than 1'.format(min_samples_leaf)) 
255  253  self.root.partition_start = 0 

256  254  self.root.partition_stop = n_samples 

257  255  
258    if self.max_leaf_nodes is not None and self.max_leaf_nodes == 1: 

259    self._finalize_leaf(self.root) 

260    return 

261  256  if self.root.n_samples < 2 * self.min_samples_leaf: 

262  257  # Do not even bother computing any splitting statistics. 

263  258  self._finalize_leaf(self.root) 
298  293  right : TreeNode 

299  294  The resulting right child. 

300  295  """ 

301    if not self.splittable_nodes: 

302    raise StopIteration("No more splittable nodes") 

303    
304  296  # Consider the node with the highest loss reduction (a.k.a. gain) 

305  297  node = heappop(self.splittable_nodes) 

306  298 
503  503  The maximum number of iterations of the boosting process, i.e. the 

504  504  maximum number of trees. 

505  505  max_leaf_nodes : int or None, optional (default=31) 

506    The maximum number of leaves for each tree. If None, there is no 

507    maximum limit. 

506  +  The maximum number of leaves for each tree. Must be strictly greater 

507  +  than 1. If None, there is no maximum limit. 

508  508  max_depth : int or None, optional (default=None) 

509  509  The maximum depth of each tree. The depth of a tree is the number of 

510    nodes to go from the root to the deepest leaf. Depth isn't constrained 

511    by default. 

510  +  nodes to go from the root to the deepest leaf. Must be strictly greater 

511  +  than 1. Depth isn't constrained by default. 

512  512  min_samples_leaf : int, optional (default=20) 

513  513  The minimum number of samples per leaf. 

514  514  l2_regularization : float, optional (default=0) 
654  654  maximum number of trees for binary classification. For multiclass 

655  655  classification, `n_classes` trees per iteration are built. 

656  656  max_leaf_nodes : int or None, optional (default=31) 

657    The maximum number of leaves for each tree. If None, there is no 

658    maximum limit. 

657  +  The maximum number of leaves for each tree. Must be strictly greater 

658  +  than 1. If None, there is no maximum limit. 

659  659  max_depth : int or None, optional (default=None) 

660  660  The maximum depth of each tree. The depth of a tree is the number of 

661    nodes to go from the root to the deepest leaf. Depth isn't constrained 

662    by default. 

661  +  nodes to go from the root to the deepest leaf. Must be strictly greater 

662  +  than 1. Depth isn't constrained by default. 

663  663  min_samples_leaf : int, optional (default=20) 

664  664  The minimum number of samples per leaf. 

665  665  l2_regularization : float, optional (default=0) 
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