class_docstr: A trained model using C++ implementation that is ready to use for classification or export to
  CoreML.
  
  This model should not be constructed directly.
class_docstr_tok: ["A","trained","model","using","C++","implementation","that","is","ready","to","use","for","classification","or","export","to","CoreML",".","This","model","should","not","be","constructed","directly","."]
docstr: Evaluate the model by making predictions of target values and comparing
  these to actual values.
  
  Parameters
  ----------
  dataset : SFrame
      Dataset of new observations. Must include columns with the same
      names as the session_id, target and features used for model training.
      Additional columns are ignored.
  
  metric : str, optional
      Name of the evaluation metric.  Possible values are:
  
      - 'auto'             : Returns all available metrics.
      - 'accuracy'         : Classification accuracy (micro average).
      - 'auc'              : Area under the ROC curve (macro average)
      - 'precision'        : Precision score (macro average)
      - 'recall'           : Recall score (macro average)
      - 'f1_score'         : F1 score (macro average)
      - 'log_loss'         : Log loss
      - 'confusion_matrix' : An SFrame with counts of possible
                             prediction/true label combinations.
      - 'roc_curve'        : An SFrame containing information needed for an
                             ROC curve
  
  Returns
  -------
  out : dict
      Dictionary of evaluation results where the key is the name of the
      evaluation metric (e.g. `accuracy`) and the value is the evaluation
      score.
  
  See Also
  ----------
  create, predict
  
  Examples
  ----------
  .. sourcecode:: python
  
    >>> results = model.evaluate(data)
    >>> print results['accuracy']
docstr_tok: ["Evaluate","the","model","by","making","predictions","of","target","values","and","comparing","these","to","actual","values",".","Parameters","--","--","--","--","--","dataset",":","SFrame","Dataset","of","new","observations",".","Must","include","columns","with","the","same","names","as","the","session_id",",","target","and","features","used","for","model","training",".","Additional","columns","are","ignored",".","metric",":","str",",","optional","Name","of","the","evaluation","metric",".","Possible","values","are",":","-","'auto","'",":","Returns","all","available","metrics",".","-","'accuracy","'",":","Classification","accuracy","(","micro","average",")",".","-","'auc","'",":","Area","under","the","ROC","curve","(","macro","average",")","-","'precision","'",":","Precision","score","(","macro","average",")","-","'recall","'",":","Recall","score","(","macro","average",")","-","'f1_score","'",":","F1","score","(","macro","average",")","-","'log_loss","'",":","Log","loss","-","'confusion_matrix","'",":","An","SFrame","with","counts","of","possible","prediction\/true","label","combinations",".","-","'roc_curve","'",":","An","SFrame","containing","information","needed","for","an","ROC","curve","Returns","--","--","--","-","out",":","dict","Dictionary","of","evaluation","results","where","the","key","is","the","name","of","the","evaluation","metric","(","e.g",".","`","accuracy","`",")","and","the","value","is","the","evaluation","score",".","See","Also","--","--","--","--","--","create",",","predict","Examples","--","--","--","--","--","..","sourcecode",":",":","python",">",">",">","results","=","model.evaluate","(","data",")",">",">",">","print","results","[","'accuracy","'","]"]
code: def evaluate(self, dataset, metric="auto"):
      """
      Evaluate the model by making predictions of target values and comparing
      these to actual values.
  
      Parameters
      ----------
      dataset : SFrame
          Dataset of new observations. Must include columns with the same
          names as the session_id, target and features used for model training.
          Additional columns are ignored.
  
      metric : str, optional
          Name of the evaluation metric.  Possible values are:
  
          - 'auto'             : Returns all available metrics.
          - 'accuracy'         : Classification accuracy (micro average).
          - 'auc'              : Area under the ROC curve (macro average)
          - 'precision'        : Precision score (macro average)
          - 'recall'           : Recall score (macro average)
          - 'f1_score'         : F1 score (macro average)
          - 'log_loss'         : Log loss
          - 'confusion_matrix' : An SFrame with counts of possible
                                 prediction/true label combinations.
          - 'roc_curve'        : An SFrame containing information needed for an
                                 ROC curve
  
      Returns
      -------
      out : dict
          Dictionary of evaluation results where the key is the name of the
          evaluation metric (e.g. `accuracy`) and the value is the evaluation
          score.
  
      See Also
      ----------
      create, predict
  
      Examples
      ----------
      .. sourcecode:: python
  
        >>> results = model.evaluate(data)
        >>> print results['accuracy']
      """
      return self.__proxy__.evaluate(dataset, metric)
  
imports: from __future__ import absolute_import
  from __future__ import print_function
  from __future__ import division
  import time
  import six
  from turicreate import SFrame
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits import _coreml_utils
  import turicreate.toolkits._feature_engineering._internal_utils
  from turicreate.toolkits._main import ToolkitError
  from turicreate._deps.minimal_package import _minimal_package_import_check
  from turicreate.toolkits._model import Model
label_desc: Use image node_id 8 for calling the ActivityClassifier obj's underlying member method code with example usage: obj.evaluate(dataset, metric) and returns: self