code: def fbeta_score(targets, predictions, beta=1.0, average="macro"):
      r"""
      Compute the F-beta score. The F-beta score is the weighted harmonic mean of
      precision and recall. The score lies in the range [0,1] with 1 being ideal
      and 0 being the worst.
  
      The `beta` value is the weight given to `precision` vs `recall` in the
      combined score. `beta=0` considers only precision, as `beta` increases, more
      weight is given to recall with `beta > 1` favoring recall over precision.
  
      The F-beta score is defined as:
  
          .. math::
              f_{\beta} = (1 + \beta^2) \times \frac{(p \times r)}{(\beta^2 p + r)}
  
      Where :math:`p` is the precision and :math:`r` is the recall.
  
      Parameters
      ----------
      targets : SArray
          An SArray of ground truth class labels. Can be of any type except
          float.
  
      predictions : SArray
          The prediction that corresponds to each target value.  This SArray must
          have the same length as ``targets`` and must be of the same type
          as the ``targets`` SArray.
  
      beta: float
          Weight of the `precision` term in the harmonic mean.
  
      average : string, [None, 'macro' (default), 'micro']
          Metric averaging strategies for multiclass classification. Averaging
          strategies can be one of the following:
  
              - None: No averaging is performed and a single metric is returned
                for each class.
              - 'micro': Calculate metrics globally by counting the total true
                positives, false negatives and false positives.
              - 'macro': Calculate metrics for each label, and find their
                unweighted mean. This does not take label imbalance into account.
  
          For a more precise definition of `micro` and `macro` averaging refer
          to [1] below.
  
      Returns
      -------
      out : float (for binary classification) or dict[float] (for multi-class, average=None)
          Score for the positive class (for binary classification) or an average
          score for each class for multi-class classification.  If
          `average=None`, then a dictionary is returned where the key is the
          class label and the value is the score for the corresponding class
          label.
  
      Notes
      -----
       - For binary classification, if the target label is of type "string",
         then the labels are sorted alphanumerically and the largest label is
         chosen as the "positive" label.  For example, if the classifier labels
         are {"cat", "dog"}, then "dog" is chosen as the positive label for the
         binary classification case.
  
  
      See Also
      --------
      confusion_matrix, accuracy, precision, recall, f1_score
  
      Examples
      --------
  
      .. sourcecode:: python
  
          # Targets and Predictions
          >>> targets = turicreate.SArray([0, 1, 2, 3, 0, 1, 2, 3])
          >>> predictions = turicreate.SArray([1, 0, 2, 1, 3, 1, 0, 1])
  
          # Micro average of the F-Beta score
          >>> turicreate.evaluation.fbeta_score(targets, predictions,
          ...                                 beta=2.0, average = 'micro')
          0.25
  
          # Macro average of the F-Beta score
          >>> turicreate.evaluation.fbeta_score(targets, predictions,
          ...                                 beta=2.0, average = 'macro')
          0.24305555555555558
  
          # F-Beta score for each class.
          >>> turicreate.evaluation.fbeta_score(targets, predictions,
          ...                                 beta=2.0, average = None)
          {0: 0.0, 1: 0.4166666666666667, 2: 0.5555555555555556, 3: 0.0}
  
      This metric also works when the targets are of type `str`
  
      .. sourcecode:: python
  
          # Targets and Predictions
          >>> targets = turicreate.SArray(
          ...      ["cat", "dog", "foosa", "snake", "cat", "dog", "foosa", "snake"])
          >>> predictions = turicreate.SArray(
          ...      ["dog", "cat", "foosa", "dog", "snake", "dog", "cat", "dog"])
  
          # Micro average of the F-Beta score
          >>> turicreate.evaluation.fbeta_score(targets, predictions,
          ...                                 beta=2.0, average = 'micro')
          0.25
  
          # Macro average of the F-Beta score
          >>> turicreate.evaluation.fbeta_score(targets, predictions,
          ...                                 beta=2.0, average = 'macro')
          0.24305555555555558
  
          # F-Beta score for each class.
          >>> turicreate.evaluation.fbeta_score(targets, predictions,
          ...                                 beta=2.0, average = None)
          {'cat': 0.0, 'dog': 0.4166666666666667, 'foosa': 0.5555555555555556, 'snake': 0.0}
  
      References
      ----------
      - [1] Sokolova, Marina, and Guy Lapalme. "A systematic analysis of
        performance measures for classification tasks." Information Processing &
        Management 45.4 (2009): 427-437.
  
      """
      _supervised_evaluation_error_checking(targets, predictions)
      _check_categorical_option_type(
          "average", average, ["micro", "macro", None]
      )
      _check_same_type_not_float(targets, predictions)
  
      opts = {"beta": beta, "average": average}
      return _turicreate.extensions._supervised_streaming_evaluator(
          targets, predictions, "fbeta_score", opts
      )
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  from turicreate.toolkits._internal_utils import _raise_error_if_not_sarray, _check_categorical_option_type
  from turicreate.toolkits._main import ToolkitError
label_desc: Use image node_id 11 for calling a global function with example usage: fbeta_score(targets, predictions, beta, average) and returns: _turicreate