docstr: Composite of multiple classifier related components, on a single tab:
      - goodness of fit component
      - one other model quality indicator: 'metrics', 'residuals' or'residuals_vs_col'
      - shap importance
      - shap dependence
      - index selector
      - index prediction summary
      - index shap contribution graph
  Args:
      explainer (Explainer): explainer object constructed with either
                  ClassifierExplainer() or RegressionExplainer()
      title (str, optional): Title of tab or page. Defaults to
                  "Simple Classification Stats".
      name (str, optional): unique name to add to Component elements.
                  If None then random uuid is generated to make sure
                  it's unique. Defaults to None.
      hide_title (bool, optional): hide the title. Defaults to False.
      regression_custom_component (str, optional): custom classifier quality
          indicator supported by the ClassifierExplainer object. Valid values are:
          'metrics', 'residuals' or'vs_col'
      hide_goodness_of_fit (bool, optional): hide goodness of fit component
      hide_regression_custom_component (bool, optional): hide the chosen
          regression_custom_component
      hide_shapsummary (bool, optional): hide ShapSummaryComponent
      hide_shapdependence (bool, optional): hide ShapDependenceComponent
      hide_predindexselector (bool, optional): hide RegressionRandomIndexComponent
          or RegressionRandomIndexComponent
      hide_predictionsummary (bool, optional): hide RegressionPredictionSummaryComponent
          or RegressionPredictionSummaryComponent
      hide_contributiongraph (bool, optional): hide ShapContributionsGraphComponent
code: def __init__(
      self,
      explainer,
      title="Simple Regression Explainer",
      name=None,
      hide_title=False,
      regression_custom_component="vs_col",
      hide_goodness_of_fit=False,
      hide_regression_custom_component=False,
      hide_shapsummary=False,
      hide_shapdependence=False,
      hide_predindexselector=False,
      hide_predictionsummary=False,
      hide_contributiongraph=False,
      **kwargs,
  ):
      """Composite of multiple classifier related components, on a single tab:
          - goodness of fit component
          - one other model quality indicator: 'metrics', 'residuals' or'residuals_vs_col'
          - shap importance
          - shap dependence
          - index selector
          - index prediction summary
          - index shap contribution graph
      Args:
          explainer (Explainer): explainer object constructed with either
                      ClassifierExplainer() or RegressionExplainer()
          title (str, optional): Title of tab or page. Defaults to
                      "Simple Classification Stats".
          name (str, optional): unique name to add to Component elements.
                      If None then random uuid is generated to make sure
                      it's unique. Defaults to None.
          hide_title (bool, optional): hide the title. Defaults to False.
          regression_custom_component (str, optional): custom classifier quality
              indicator supported by the ClassifierExplainer object. Valid values are:
              'metrics', 'residuals' or'vs_col'
          hide_goodness_of_fit (bool, optional): hide goodness of fit component
          hide_regression_custom_component (bool, optional): hide the chosen
              regression_custom_component
          hide_shapsummary (bool, optional): hide ShapSummaryComponent
          hide_shapdependence (bool, optional): hide ShapDependenceComponent
          hide_predindexselector (bool, optional): hide RegressionRandomIndexComponent
              or RegressionRandomIndexComponent
          hide_predictionsummary (bool, optional): hide RegressionPredictionSummaryComponent
              or RegressionPredictionSummaryComponent
          hide_contributiongraph (bool, optional): hide ShapContributionsGraphComponent
      """
      super().__init__(explainer, title, name)
  
      self.goodness_of_fit = PredictedVsActualComponent(
          explainer, **kwargs
      )
  
      # select custom classifier report metric
      if regression_custom_component == "metrics":
          self.regression_custom_component = (
              RegressionModelSummaryComponent(explainer, **kwargs)
          )
      elif regression_custom_component == "residuals":
          self.regression_custom_component = ResidualsComponent(
              explainer, **kwargs
          )
      elif regression_custom_component == "vs_col":
          self.regression_custom_component = RegressionVsColComponent(
              explainer, **update_params(kwargs, display="predicted")
          )
      else:
          raise ValueError(
              "ERROR: SimplifiedRegressionDashboard parameter "
              "regression_custom_component should be in {'metrics', 'residuals', 'vs_col'}"
              f" but you passed {regression_custom_component}!"
          )
  
      # SHAP summary & dependence
      self.shap_summary = ShapSummaryComponent(
          explainer,
          **update_params(
              kwargs,
              title="Shap Feature Importances",
              hide_index=True,
              depth=None,
              hide_depth=True,
          ),
      )
      self.shap_dependence = ShapDependenceComponent(
          explainer, **update_params(kwargs, hide_index=True)
      )
  
      # SHAP contribution, along with prediction summary
      self.index = RegressionRandomIndexComponent(explainer, **kwargs)
      self.summary = RegressionPredictionSummaryComponent(
          explainer, hide_index=True, **kwargs
      )
      self.contributions = ShapContributionsGraphComponent(
          explainer,
          **update_params(kwargs, hide_index=True, hide_depth=True),
      )
  
      self.connector = ShapSummaryDependenceConnector(
          self.shap_summary, self.shap_dependence
      )
      self.index_connector = IndexConnector(
          self.index, [self.summary, self.contributions]
      )
  
imports: import dash_bootstrap_components
  from dash import html
  from ..explainers import RandomForestExplainer, XGBExplainer
  from ..dashboard_methods import *
  from .classifier_components import *
  from .regression_components import *
  from .overview_components import *
  from .connectors import *
  from .shap_components import *
  from .decisiontree_components import *
  from ..None import to_html
label_desc: Use image node_id 1 to create a new SimplifiedRegressionComposite object from inherited base classes: ExplainerComponent with example: obj = SimplifiedRegressionComposite(explainer, title, name, hide_title, regression_custom_component, hide_goodness_of_fit, hide_regression_custom_component, hide_shapsummary, hide_shapdependence, hide_predindexselector, hide_predictionsummary, hide_contributiongraph)