class_docstr: The gradient boosted trees model can be used as a classifier for predictive
  tasks.
  
  The prediction is based on a collection of base learners i.e
  `decision tree classifiers <http://en.wikipedia.org/wiki/Decision_tree_learning>`_
  and combines them through a technique called `gradient boosting
  <http://en.wikipedia.org/wiki/Gradient_boosting>`_.
  
  Different from linear models like logistic regression or SVM, gradient
  boost trees can model non-linear interactions between the features and the
  target. This model is suitable for handling numerical features and
  categorical features with tens of categories but is less suitable for highly
  sparse features (text data), or with categorical variables that encode a
  large number of categories.
  
  This model cannot be constructed directly.  Instead, use
  :func:`turicreate.boosted_trees_classifier.create` to create an instance of
  this model.
  Additional details on parameter options and code samples are available in
  the documentation for the create function.
  
  See Also
  --------
  create
docstr: Get the value of a given field. The following table describes each
  of the fields below.
  
  +-------------------------+--------------------------------------------------------------------------------+
  | Field                   | Description                                                                    |
  +=========================+================================================================================+
  | column_subsample        | Percentage of the columns for training each individual tree                    |
  +-------------------------+--------------------------------------------------------------------------------+
  | features                | Names of the feature columns                                                   |
  +-------------------------+--------------------------------------------------------------------------------+
  | max_depth               | The maximum depth of the individual decision trees                             |
  +-------------------------+--------------------------------------------------------------------------------+
  | max_iterations          | Maximum number of iterations for training (one tree is trained per iteration)  |
  +-------------------------+--------------------------------------------------------------------------------+
  | min_child_weight        | Minimum weight assigned to leaf nodes                                          |
  +-------------------------+--------------------------------------------------------------------------------+
  | min_loss_reduction      | Minimum loss reduction required for splitting a node                           |
  +-------------------------+--------------------------------------------------------------------------------+
  | num_features            | Number of feature columns in the model                                         |
  +-------------------------+--------------------------------------------------------------------------------+
  | num_unpacked_features   | Number of features in the model (including unpacked dict/list type columns)    |
  +-------------------------+--------------------------------------------------------------------------------+
  | num_examples            | Number of training examples                                                    |
  +-------------------------+--------------------------------------------------------------------------------+
  | num_validation_examples | Number of validation examples                                                  |
  +-------------------------+--------------------------------------------------------------------------------+
  | row_subsample           | Percentage of the rows sampled for training each individual tree               |
  +-------------------------+--------------------------------------------------------------------------------+
  | step_size               | Step_size used for combining the weight of individual trees                    |
  +-------------------------+--------------------------------------------------------------------------------+
  | target                  | Name of the target column                                                      |
  +-------------------------+--------------------------------------------------------------------------------+
  | training_accuracy       | Classification accuracy measured on the training data                          |
  +-------------------------+--------------------------------------------------------------------------------+
  | training_time           | Time spent on training the model in seconds                                    |
  +-------------------------+--------------------------------------------------------------------------------+
  | trees_json              | Tree encoded using JSON                                                        |
  +-------------------------+--------------------------------------------------------------------------------+
  | validation_accuracy     | Classification accuracy measured on the validation set                         |
  +-------------------------+--------------------------------------------------------------------------------+
  | unpacked_features       | Feature names (including expanded list/dict features)                          |
  +-------------------------+--------------------------------------------------------------------------------+
  | random_seed             | Seed for row and column subselection                                           |
  +-------------------------+--------------------------------------------------------------------------------+
  | metric                  | Performance metric(s) that are tracked during training                         |
  +-------------------------+--------------------------------------------------------------------------------+
  
  
  Parameters
  ----------
  field : string
      Name of the field to be retrieved.
  
  Returns
  -------
  out : [various]
      The current value of the requested field.
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code: def _get(self, field):
      """
      Get the value of a given field. The following table describes each
      of the fields below.
  
      +-------------------------+--------------------------------------------------------------------------------+
      | Field                   | Description                                                                    |
      +=========================+================================================================================+
      | column_subsample        | Percentage of the columns for training each individual tree                    |
      +-------------------------+--------------------------------------------------------------------------------+
      | features                | Names of the feature columns                                                   |
      +-------------------------+--------------------------------------------------------------------------------+
      | max_depth               | The maximum depth of the individual decision trees                             |
      +-------------------------+--------------------------------------------------------------------------------+
      | max_iterations          | Maximum number of iterations for training (one tree is trained per iteration)  |
      +-------------------------+--------------------------------------------------------------------------------+
      | min_child_weight        | Minimum weight assigned to leaf nodes                                          |
      +-------------------------+--------------------------------------------------------------------------------+
      | min_loss_reduction      | Minimum loss reduction required for splitting a node                           |
      +-------------------------+--------------------------------------------------------------------------------+
      | num_features            | Number of feature columns in the model                                         |
      +-------------------------+--------------------------------------------------------------------------------+
      | num_unpacked_features   | Number of features in the model (including unpacked dict/list type columns)    |
      +-------------------------+--------------------------------------------------------------------------------+
      | num_examples            | Number of training examples                                                    |
      +-------------------------+--------------------------------------------------------------------------------+
      | num_validation_examples | Number of validation examples                                                  |
      +-------------------------+--------------------------------------------------------------------------------+
      | row_subsample           | Percentage of the rows sampled for training each individual tree               |
      +-------------------------+--------------------------------------------------------------------------------+
      | step_size               | Step_size used for combining the weight of individual trees                    |
      +-------------------------+--------------------------------------------------------------------------------+
      | target                  | Name of the target column                                                      |
      +-------------------------+--------------------------------------------------------------------------------+
      | training_accuracy       | Classification accuracy measured on the training data                          |
      +-------------------------+--------------------------------------------------------------------------------+
      | training_time           | Time spent on training the model in seconds                                    |
      +-------------------------+--------------------------------------------------------------------------------+
      | trees_json              | Tree encoded using JSON                                                        |
      +-------------------------+--------------------------------------------------------------------------------+
      | validation_accuracy     | Classification accuracy measured on the validation set                         |
      +-------------------------+--------------------------------------------------------------------------------+
      | unpacked_features       | Feature names (including expanded list/dict features)                          |
      +-------------------------+--------------------------------------------------------------------------------+
      | random_seed             | Seed for row and column subselection                                           |
      +-------------------------+--------------------------------------------------------------------------------+
      | metric                  | Performance metric(s) that are tracked during training                         |
      +-------------------------+--------------------------------------------------------------------------------+
  
  
      Parameters
      ----------
      field : string
          Name of the field to be retrieved.
  
      Returns
      -------
      out : [various]
          The current value of the requested field.
      """
      return super(_Classifier, self)._get(field)
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  from turicreate.toolkits._supervised_learning import Classifier
  import turicreate.toolkits._supervised_learning
  from turicreate.toolkits._internal_utils import _toolkit_repr_print
  from turicreate.toolkits._internal_utils import _raise_error_evaluation_metric_is_valid
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sframe
  from turicreate.toolkits._internal_utils import _check_categorical_option_type
  from turicreate.toolkits._tree_model_mixin import TreeModelMixin
  from turicreate.util import _make_internal_url
label_desc: Use image node_id 5 for calling the BoostedTreesClassifier obj's underlying member method code with example usage: obj._get(field) and returns: super