class_docstr: Wrapper around sklearn greedy tree classifier
      
class_docstr_tok: ["Wrapper","around","sklearn","greedy","tree","classifier"]
docstr: Build a decision tree classifier from the training set (X, y).
  Parameters
  ----------
  X : {array-like, sparse matrix} of shape (n_samples, n_features)
      The training input samples. Internally, it will be converted to
      ``dtype=np.float32`` and if a sparse matrix is provided
      to a sparse ``csc_matrix``.
  y : array-like of shape (n_samples,) or (n_samples, n_outputs)
      The target values (class labels) as integers or strings.
  feature_names : array-like of shape (n_features)
      The names of the features
  sample_weight : array-like of shape (n_samples,), default=None
      Sample weights. If None, then samples are equally weighted. Splits
      that would create child nodes with net zero or negative weight are
      ignored while searching for a split in each node. Splits are also
      ignored if they would result in any single class carrying a
      negative weight in either child node.
  check_input : bool, default=True
      Allow to bypass several input checking.
      Don't use this parameter unless you know what you do.
  
  Returns
  -------
  self : DecisionTreeClassifier
      Fitted estimator.
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code: def fit(
      self,
      X,
      y,
      feature_names=None,
      sample_weight=None,
      check_input=True,
  ):
      """Build a decision tree classifier from the training set (X, y).
      Parameters
      ----------
      X : {array-like, sparse matrix} of shape (n_samples, n_features)
          The training input samples. Internally, it will be converted to
          ``dtype=np.float32`` and if a sparse matrix is provided
          to a sparse ``csc_matrix``.
      y : array-like of shape (n_samples,) or (n_samples, n_outputs)
          The target values (class labels) as integers or strings.
      feature_names : array-like of shape (n_features)
          The names of the features
      sample_weight : array-like of shape (n_samples,), default=None
          Sample weights. If None, then samples are equally weighted. Splits
          that would create child nodes with net zero or negative weight are
          ignored while searching for a split in each node. Splits are also
          ignored if they would result in any single class carrying a
          negative weight in either child node.
      check_input : bool, default=True
          Allow to bypass several input checking.
          Don't use this parameter unless you know what you do.
  
      Returns
      -------
      self : DecisionTreeClassifier
          Fitted estimator.
      """
      X, y, feature_names = check_fit_arguments(
          self, X, y, feature_names
      )
      super().fit(
          X, y, sample_weight=sample_weight, check_input=check_input
      )
      self._set_complexity()
  
file_path: imodels/imodels/tree/cart_wrapper.py
imports: from sklearn.tree import DecisionTreeClassifier, export_text, DecisionTreeRegressor
  from imodels.util.arguments import check_fit_arguments
  from imodels.util.tree import compute_tree_complexity
label_desc: Use image node_id 1 for calling the GreedyTreeClassifier obj's underlying member method code with example usage: obj.fit(X, y, feature_names, sample_weight, check_input) without return types