code: def precision(targets, predictions, average="macro"):
      r"""
  
      Compute the precision score for classification tasks. The precision score
      quantifies the ability of a classifier to not label a `negative` example as
      `positive`. The precision score can be interpreted as the probability that
      a `positive` prediction made by the classifier is `positive`. The score is
      in the range [0,1] with 0 being the worst, and 1 being perfect.
  
  
      The precision score is defined as the ratio:
          .. math::
              \frac{tp}{tp + fp}
  
      where `tp` is the number of true positives and `fp` the number of false
      positives.
  
      Parameters
      ----------
      targets : SArray
          Ground truth class labels.
  
      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.
  
      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, and false positives.
              - 'macro': Calculate metrics for each label, and find their
                unweighted mean. This does not take label imbalance into account.
  
      Returns
      -------
      out : float (for binary classification) or dict[float]
          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, when 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, 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 precision scores for each class.
          >>> turicreate.evaluation.precision(targets, predictions,
          ...                               average = 'micro')
          0.25
  
          # Macro average of the precision scores for each class.
          >>> turicreate.evaluation.precision(targets, predictions,
          ...                              average = 'macro')
          0.3125
  
          # Precision score for each class.
          >>> turicreate.evaluation.precision(targets, predictions,
          ...                               average = None)
          {0: 0.0, 1: 0.25, 2: 1.0, 3: 0.0}
  
      This metric also works for string classes.
  
      .. 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 precision scores for each class.
          >>> turicreate.evaluation.precision(targets, predictions,
          ...                               average = 'micro')
          0.25
  
          # Macro average of the precision scores for each class.
          >>> turicreate.evaluation.precision(targets, predictions,
          ...                              average = 'macro')
          0.3125
  
          # Precision score for each class.
          >>> turicreate.evaluation.precision(targets, predictions,
          ...                               average = None)
          {0: 0.0, 1: 0.25, 2: 1.0, 3: 0.0}
      """
      _supervised_evaluation_error_checking(targets, predictions)
      _check_categorical_option_type(
          "average", average, ["micro", "macro", None]
      )
      _check_same_type_not_float(targets, predictions)
      opts = {"average": average}
      return _turicreate.extensions._supervised_streaming_evaluator(
          targets, predictions, "precision", 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 13 for calling a global function with example usage: precision(targets, predictions, average) and returns: _turicreate