docstr: Composite of multiple classifier related components:
      - precision graph
      - confusion matrix
      - lift curve
      - classification graph
      - roc auc graph
      - pr auc graph
  
  Args:
      explainer (Explainer): explainer object constructed with either
                  ClassifierExplainer() or RegressionExplainer()
      title (str, optional): Title of tab or page. Defaults to
                  "Decision Trees".
      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 title. Defaults to True.
      hide_selector (bool, optional): hide all pos label selectors. Defaults to True.
      hide_globalcutoff (bool, optional): hide CutoffPercentileComponent
      hide_modelsummary (bool, optional): hide ClassifierModelSummaryComponent
      hide_confusionmatrix (bool, optional): hide ConfusionMatrixComponent
      hide_precision (bool, optional): hide PrecisionComponent
      hide_classification (bool, optional): hide ClassificationComponent
      hide_rocauc (bool, optional): hide RocAucComponent
      hide_prauc (bool, optional): hide PrAucComponent
      hide_liftcurve (bool, optional): hide LiftCurveComponent
      hide_cumprecision (bool, optional): hide CumulativePrecisionComponent
      pos_label ({int, str}, optional): initial pos label. Defaults to explainer.pos_label
      bin_size (float, optional): bin_size for precision plot. Defaults to 0.1.
      quantiles (int, optional): number of quantiles for precision plot. Defaults to 10.
      cutoff (float, optional): initial cutoff. Defaults to 0.5.
code: def __init__(
      self,
      explainer,
      title="Classification Stats",
      name=None,
      hide_title=True,
      hide_selector=True,
      hide_globalcutoff=False,
      hide_modelsummary=False,
      hide_confusionmatrix=False,
      hide_precision=False,
      hide_classification=False,
      hide_rocauc=False,
      hide_prauc=False,
      hide_liftcurve=False,
      hide_cumprecision=False,
      pos_label=None,
      bin_size=0.1,
      quantiles=10,
      cutoff=0.5,
      **kwargs,
  ):
      """Composite of multiple classifier related components:
          - precision graph
          - confusion matrix
          - lift curve
          - classification graph
          - roc auc graph
          - pr auc graph
  
      Args:
          explainer (Explainer): explainer object constructed with either
                      ClassifierExplainer() or RegressionExplainer()
          title (str, optional): Title of tab or page. Defaults to
                      "Decision Trees".
          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 title. Defaults to True.
          hide_selector (bool, optional): hide all pos label selectors. Defaults to True.
          hide_globalcutoff (bool, optional): hide CutoffPercentileComponent
          hide_modelsummary (bool, optional): hide ClassifierModelSummaryComponent
          hide_confusionmatrix (bool, optional): hide ConfusionMatrixComponent
          hide_precision (bool, optional): hide PrecisionComponent
          hide_classification (bool, optional): hide ClassificationComponent
          hide_rocauc (bool, optional): hide RocAucComponent
          hide_prauc (bool, optional): hide PrAucComponent
          hide_liftcurve (bool, optional): hide LiftCurveComponent
          hide_cumprecision (bool, optional): hide CumulativePrecisionComponent
          pos_label ({int, str}, optional): initial pos label. Defaults to explainer.pos_label
          bin_size (float, optional): bin_size for precision plot. Defaults to 0.1.
          quantiles (int, optional): number of quantiles for precision plot. Defaults to 10.
          cutoff (float, optional): initial cutoff. Defaults to 0.5.
      """
      super().__init__(explainer, title, name)
  
      self.summary = ClassifierModelSummaryComponent(
          explainer,
          hide_selector=hide_selector,
          pos_label=pos_label,
          **kwargs,
      )
      self.precision = PrecisionComponent(
          explainer,
          hide_selector=hide_selector,
          pos_label=pos_label,
          **kwargs,
      )
      self.confusionmatrix = ConfusionMatrixComponent(
          explainer,
          hide_selector=hide_selector,
          pos_label=pos_label,
          **kwargs,
      )
      self.cumulative_precision = CumulativePrecisionComponent(
          explainer,
          hide_selector=hide_selector,
          pos_label=pos_label,
          **kwargs,
      )
      self.liftcurve = LiftCurveComponent(
          explainer,
          hide_selector=hide_selector,
          pos_label=pos_label,
          **kwargs,
      )
      self.classification = ClassificationComponent(
          explainer,
          hide_selector=hide_selector,
          pos_label=pos_label,
          **kwargs,
      )
      self.rocauc = RocAucComponent(
          explainer,
          hide_selector=hide_selector,
          pos_label=pos_label,
          **kwargs,
      )
      self.prauc = PrAucComponent(
          explainer,
          hide_selector=hide_selector,
          pos_label=pos_label,
          **kwargs,
      )
  
      self.cutoffpercentile = CutoffPercentileComponent(
          explainer,
          name=self.name + "8",
          hide_selector=hide_selector,
          pos_label=pos_label,
          cutoff=cutoff,
          **kwargs,
      )
      self.cutoffconnector = CutoffConnector(
          self.cutoffpercentile,
          [
              self.summary,
              self.precision,
              self.confusionmatrix,
              self.liftcurve,
              self.cumulative_precision,
              self.classification,
              self.rocauc,
              self.prauc,
          ],
      )
  
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 ClassifierModelStatsComposite object from inherited base classes: ExplainerComponent with example: obj = ClassifierModelStatsComposite(explainer, title, name, hide_title, hide_selector, hide_globalcutoff, hide_modelsummary, hide_confusionmatrix, hide_precision, hide_classification, hide_rocauc, hide_prauc, hide_liftcurve, hide_cumprecision, pos_label, bin_size, quantiles, cutoff)