class_docstr: Decision tree classifier powered by Intel(R) oneAPI Data Analytics Library.
  
     https://www.intel.com/content/www/us/en/developer/tools/oneapi/
     onedal-documentation.html
  
  Parameters
  ----------
  max_depth : int or None, default=None
      Depth of the tree fitten to data. None corresponds to unlimited depth.
  
  min_observations_in_leaf_node : int, default=10
      The number of estimators in the ensemble.
  
  estimator_params : list of str, default=tuple()
      The list of attributes to use as parameters when instantiating a
      new base estimator. If none are given, default parameters are used.
  
  Attributes
  ----------
  base_estimator_ : estimator
      The base estimator from which the ensemble is grown.
  
  estimators_ : list of estimators
      The collection of fitted base estimators.
  
  
  Training:
      inputs: dataForPruning, labelsForPruning
      parameters: fptype, method, nClasses, splitCriterion, pruning,
                  maxTreeDepth, minObservationsInLeafNodes
  
  Prediction:
      parameters: fptype, method, nBins, nClasses,
      resultsToEvaluate (computeClassesLabels|computeClassProbabilities)
      N.B.: The only supported value for current version of the library is nBins=1.
            nBins is the number of bins used to compute probabilities of the
            observations belonging to the class.
class_docstr_tok: ["Decision","tree","classifier","powered","by","Intel","(","R",")","oneAPI","Data","Analytics","Library",".","https",":","\/\/www.intel.com\/content\/www\/us\/en\/developer\/tools\/oneapi\/","onedal-documentation.html","Parameters","--","--","--","--","--","max_depth",":","int","or","None",",","default=None","Depth","of","the","tree","fitten","to","data",".","None","corresponds","to","unlimited","depth",".","min_observations_in_leaf_node",":","int",",","default=10","The","number","of","estimators","in","the","ensemble",".","estimator_params",":","list","of","str",",","default=tuple","(",")","The","list","of","attributes","to","use","as","parameters","when","instantiating","a","new","base","estimator",".","If","none","are","given",",","default","parameters","are","used",".","Attributes","--","--","--","--","--","base_estimator_",":","estimator","The","base","estimator","from","which","the","ensemble","is","grown",".","estimators_",":","list","of","estimators","The","collection","of","fitted","base","estimators",".","Training",":","inputs",":","dataForPruning",",","labelsForPruning","parameters",":","fptype",",","method",",","nClasses",",","splitCriterion",",","pruning",",","maxTreeDepth",",","minObservationsInLeafNodes","Prediction",":","parameters",":","fptype",",","method",",","nBins",",","nClasses",",","resultsToEvaluate","(","computeClassesLabels|computeClassProbabilities",")","N.B",".",":","The","only","supported","value","for","current","version","of","the","library","is","nBins=1",".","nBins","is","the","number","of","bins","used","to","compute","probabilities","of","the","observations","belonging","to","the","class","."]
code: def __init__(
      self,
      max_depth=None,
      min_observations_in_leaf_node=1,
      split_criterion="gini",
  ):
      self.max_depth = max_depth
      self.min_observations_in_leaf_node = min_observations_in_leaf_node
      self.split_criterion = split_criterion
  
imports: import numbers
  import warnings
  import numpy
  from scipy.sparse import issparse
  from sklearn.base import BaseEstimator, ClassifierMixin
  from sklearn.exceptions import DataConversionWarning
  from sklearn.utils.multiclass import check_classification_targets
  from sklearn.utils.validation import check_array, check_consistent_length, check_is_fitted
  import daal4py
  from .._device_offload import support_usm_ndarray
  from .._utils import getFPType, make2d
label_desc: Use image node_id 1 to create a new DecisionTreeClassifier object from inherited base classes: BaseEstimator, ClassifierMixin with example: obj = DecisionTreeClassifier(max_depth, min_observations_in_leaf_node, split_criterion)