class_docstr: Produces ROC curves.
class_docstr_tok: ["Produces","ROC","curves","."]
docstr: Initializes class.
  
  Args:
      model: model or prediction function of model (predict_proba for classification or predict for regression)
      feature_names: List of feature names.
      feature_types: List of feature types.
docstr_tok: ["Initializes","class",".","Args",":","model",":","model","or","prediction","function","of","model","(","predict_proba","for","classification","or","predict","for","regression",")","feature_names",":","List","of","feature","names",".","feature_types",":","List","of","feature","types","."]
code: def __init__(self, model, feature_names=None, feature_types=None):
      """Initializes class.
  
      Args:
          model: model or prediction function of model (predict_proba for classification or predict for regression)
          feature_names: List of feature names.
          feature_types: List of feature types.
      """
      self.model = model
      self.feature_names = feature_names
      self.feature_types = feature_types
  
imports: from itertools import count
  from ..api.base import ExplainerMixin, ExplanationMixin
  from ..utils._explanation import gen_name_from_class
  from sklearn.metrics import roc_curve, auc
  from sklearn.metrics import precision_recall_curve, average_precision_score
  import numpy
  from ..utils._clean_x import preclean_X
  from ..utils._clean_simple import clean_dimensions, typify_classification
  from ..utils._unify_predict import determine_classes, unify_predict_fn
  from ..utils._unify_data import unify_data
label_desc: Use image node_id 1 to create a new ROC object from inherited base classes: ExplainerMixin with example: obj = ROC(model, feature_names, feature_types)