class_docstr: A trained model that is ready to use for classification or export to CoreML.
  
  This model should not be constructed directly.
class_docstr_tok: ["A","trained","model","that","is","ready","to","use","for","classification","or","export","to","CoreML",".","This","model","should","not","be","constructed","directly","."]
docstr: Return a classification, for each example in the ``dataset``, using the
  trained logistic regression model. The output SFrame contains predictions
  as both class labels (0 or 1) as well as probabilities that the predicted
  value is the associated label.
  
  Parameters
  ----------
  dataset : SFrame | SArray | turicreate.Image
      Images to be classified.
      If dataset is an SFrame, it must include columns with the same
      names as the features used for model training, but does not require
      a target column. Additional columns are ignored.
  
  batch_size : int, optional
      If you are getting memory errors, try decreasing this value. If you
      have a powerful computer, increasing this value may improve performance.
  
  Returns
  -------
  out : SFrame
      An SFrame with model predictions i.e class labels and
      probabilities. If `dataset` is a single image, the return will be a
      single row (dict).
  
  See Also
  ----------
  create, evaluate, predict
  
  Examples
  ----------
  >>> classes = model.classify(data)
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code: def classify(self, dataset, batch_size=64):
      """
      Return a classification, for each example in the ``dataset``, using the
      trained logistic regression model. The output SFrame contains predictions
      as both class labels (0 or 1) as well as probabilities that the predicted
      value is the associated label.
  
      Parameters
      ----------
      dataset : SFrame | SArray | turicreate.Image
          Images to be classified.
          If dataset is an SFrame, it must include columns with the same
          names as the features used for model training, but does not require
          a target column. Additional columns are ignored.
  
      batch_size : int, optional
          If you are getting memory errors, try decreasing this value. If you
          have a powerful computer, increasing this value may improve performance.
  
      Returns
      -------
      out : SFrame
          An SFrame with model predictions i.e class labels and
          probabilities. If `dataset` is a single image, the return will be a
          single row (dict).
  
      See Also
      ----------
      create, evaluate, predict
  
      Examples
      ----------
      >>> classes = model.classify(data)
  
      """
      from array import array
  
      if not isinstance(
          dataset, (_tc.SFrame, _tc.SArray, _tc.Image, array)
      ):
          raise TypeError(
              "dataset must be either an SFrame, SArray or turicreate.Image"
          )
      if batch_size < 1:
          raise ValueError(
              "'batch_size' must be greater than or equal to 1"
          )
  
      dataset, unpack = self._canonize_input(dataset)
  
      extracted_features = self._extract_features(
          dataset, batch_size=batch_size
      )
      return unpack(self.classifier.classify(extracted_features))
  
imports: from __future__ import print_function
  from __future__ import division
  from __future__ import absolute_import
  import turicreate
  import time
  from turicreate.toolkits._model import CustomModel
  import turicreate.toolkits._internal_utils
  from turicreate.toolkits import _coreml_utils
  from turicreate.toolkits._main import ToolkitError
  from turicreate.toolkits._model import PythonProxy
  from turicreate._deps.minimal_package import _minimal_package_import_check
  from .._internal_utils import _mac_ver
  from ..None import _pre_trained_models
  from ..None import _image_feature_extractor
  from ..image_analysis import image_analysis
  from ._evaluation import Evaluation
label_desc: Use image node_id 11 for calling the ImageClassifier obj's underlying member method code with example usage: obj.classify(dataset, batch_size) and returns: unpack