class_docstr: Produces variety of regression metrics (including RMSE, R^2, etc).
class_docstr_tok: ["Produces","variety","of","regression","metrics","(","including","RMSE",",","R^2",",","etc",")","."]
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
  
file_path: interpret/python/interpret-core/interpret/perf/_regression.py
imports: from ..api.base import ExplainerMixin, ExplanationMixin
  from ..utils._explanation import gen_name_from_class
  from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
  import numpy
  from ..utils._clean_x import preclean_X
  from ..utils._clean_simple import clean_dimensions
  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 RegressionPerf object from inherited base classes: ExplainerMixin with example: obj = RegressionPerf(model, feature_names, feature_types)