code: def create(
      dataset,
      target,
      features=None,
      max_iterations=10,
      validation_set="auto",
      verbose=True,
      class_weights=None,
      random_seed=None,
      metric="auto",
      **kwargs,
  ):
      """
      Create a (binary or multi-class) classifier model of type
      :class:`~turicreate.random_forest_classifier.RandomForestClassifier` using
      an ensemble of decision trees trained on subsets of the data.
  
      Parameters
      ----------
      dataset : SFrame
          A training dataset containing feature columns and a target column.
  
      target : str
          Name of the column containing the target variable. The values in this
          column must be of string or integer type.  String target variables are
          automatically mapped to integers in alphabetical order of the variable values.
          For example, a target variable with 'cat', 'dog', and 'foosa' as possible
          values is mapped to 0, 1, and, 2 respectively.
  
      features : list[str], optional
          A list of columns names of features used for training the model.
          Defaults to None, which uses all columns in the SFrame ``dataset``
          excepting the target column..
  
      max_iterations : int, optional
          The maximum number of iterations to perform. For multi-class
          classification with K classes, each iteration will create K-1 trees.
  
      max_depth : float, optional
          Maximum depth of a tree.
  
      class_weights : {dict, `auto`}, optional
          Weights the examples in the training data according to the given class
          weights. If set to `None`, all classes are supposed to have weight one. The
          `auto` mode set the class weight to be inversely proportional to number of
          examples in the training data with the given class.
  
      min_loss_reduction : float, optional (non-negative)
          Minimum loss reduction required to make a further partition on a
          leaf node of the tree. The larger it is, the more conservative the
          algorithm will be. Must be non-negative.
  
      min_child_weight : float, optional (non-negative)
          Controls the minimum weight of each leaf node. Larger values result in
          more conservative tree learning and help prevent overfitting.
          Formally, this is minimum sum of instance weights (hessians) in each
          node. If the tree learning algorithm results in a leaf node with the
          sum of instance weights less than `min_child_weight`, tree building
          will terminate.
  
      row_subsample : float, optional
          Subsample the ratio of the training set in each iteration of tree
          construction.  This is called the bagging trick and can usually help
          prevent overfitting.  Setting this to a value of 0.5 results in the
          model randomly sampling half of the examples (rows) to grow each tree.
  
      column_subsample : float, optional
          Subsample ratio of the columns in each iteration of tree
          construction.  Like row_subsample, this can also help prevent
          model overfitting.  Setting this to a value of 0.5 results in the
          model randomly sampling half of the columns to grow each tree.
  
      validation_set : SFrame, optional
          A dataset for monitoring the model's generalization performance.
          For each row of the progress table, the chosen metrics are computed
          for both the provided training dataset and the validation_set. The
          format of this SFrame must be the same as the training set.
          By default this argument is set to 'auto' and a validation set is
          automatically sampled and used for progress printing. If
          validation_set is set to None, then no additional metrics
          are computed. This is computed once per full iteration. Large
          differences in model accuracy between the training data and validation
          data is indicative of overfitting. The default value is 'auto'.
  
      verbose : boolean, optional
          Print progress information during training (if set to true).
  
      random_seed : int, optional
          Seeds random operations such as column and row subsampling, such that
          results are reproducible.
  
      metric : str or list[str], optional
          Performance metric(s) that are tracked during training. When specified,
          the progress table will display the tracked metric(s) on training and
          validation set.
          Supported metrics are: {'accuracy', 'auc', 'log_loss'}
  
      kwargs : dict, optional
          Additional arguments for training the model.
  
          - ``model_checkpoint_path`` : str, default None
              If specified, checkpoint the model training to the given path every n iterations,
              where n is specified by ``model_checkpoint_interval``.
              For instance, if `model_checkpoint_interval` is 5, and `model_checkpoint_path` is
              set to ``/tmp/model_tmp``, the checkpoints will be saved into
              ``/tmp/model_tmp/model_checkpoint_5``, ``/tmp/model_tmp/model_checkpoint_10``, ... etc.
              Training can be resumed by setting ``resume_from_checkpoint`` to one of these checkpoints.
  
          - ``model_checkpoint_interval`` : int, default 5
              If model_check_point_path is specified,
              save the model to the given path every n iterations.
  
          - ``resume_from_checkpoint`` : str, default None
              Continues training from a model checkpoint. The model must take
              exact the same training data as the checkpointed model.
  
  
      Returns
      -------
        out : RandomForestClassifier
            A trained random forest model for classification tasks.
  
      References
      ----------
      - `Trevor Hastie's slides on Boosted Trees and Random Forest
        <http://jessica2.msri.org/attachments/10778/10778-boost.pdf>`_
  
      See Also
      --------
      RandomForestClassifier, turicreate.logistic_classifier.LogisticClassifier, turicreate.svm_classifier.SVMClassifier
  
  
      Examples
      --------
  
      .. sourcecode:: python
  
        >>> url = 'https://static.turi.com/datasets/xgboost/mushroom.csv'
        >>> data = turicreate.SFrame.read_csv(url)
  
        >>> train, test = data.random_split(0.8)
        >>> model = turicreate.random_forest_classifier.create(train, target='label')
  
        >>> predictions = model.classify(test)
        >>> results = model.evaluate(test)
      """
  
      if random_seed is not None:
          kwargs["random_seed"] = random_seed
      if "model_checkpoint_path" in kwargs:
          kwargs["model_checkpoint_path"] = _make_internal_url(
              kwargs["model_checkpoint_path"]
          )
      if "resume_from_checkpoint" in kwargs:
          kwargs["resume_from_checkpoint"] = _make_internal_url(
              kwargs["resume_from_checkpoint"]
          )
  
      model = _sl.create(
          dataset=dataset,
          target=target,
          features=features,
          model_name="random_forest_classifier",
          max_iterations=max_iterations,
          validation_set=validation_set,
          class_weights=class_weights,
          verbose=verbose,
          metric=metric,
          **kwargs,
      )
      return RandomForestClassifier(model.__proxy__)
  
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 _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 1 for calling a global function with example usage: create(dataset, target, features, max_iterations, validation_set, verbose, class_weights, random_seed, metric) and returns: RandomForestClassifier