class_docstr: Gradient boosting classification model.
      
class_docstr_tok: ["Gradient","boosting","classification","model","."]
docstr: Gets the classification results for the input sample.
  
  :param X: the input vectors, put into a matrix. The values will be 
      converted to ``dtype=np.float32``. If a sparse matrix is
      passed in, it will be converted to a sparse ``csr_matrix``.
  :type X: {array-like, sparse matrix} of shape (n_samples, n_features)
  
  :return: the predictions of class probability for each input vector.
  :rtype: *generator of ndarray of shape (n_samples, n_classes)*
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code: def classify(self, X):
      """Gets the classification results for the input sample.
  
      :param X: the input vectors, put into a matrix. The values will be
          converted to ``dtype=np.float32``. If a sparse matrix is
          passed in, it will be converted to a sparse ``csr_matrix``.
      :type X: {array-like, sparse matrix} of shape (n_samples, n_features)
  
      :return: the predictions of class probability for each input vector.
      :rtype: *generator of ndarray of shape (n_samples, n_classes)*
      """
      x = convert_data(X)
      return self.internal.classify(*get_data(x))
  
file_path: neoml/NeoML/Python/neoml/GradientBoost.py
imports: import numpy
  from .Utils import convert_data, get_data
  import neoml.PythonWrapper
label_desc: Use image node_id 3 for calling the GradientBoostClassificationModel obj's underlying member method code with example usage: obj.classify(X) and returns: self