class_docstr: Render confusion matrix for binary/multiclass classificator.
  
  This visualization depends on :py:class:`~autogluon.eda.analysis.model.AutoGluonModelEvaluator` analysis.
  
  Parameters
  ----------
  headers: bool, default = False
      if `True` then render headers
  namespace: str, default = None
      namespace to use; can be nested like `ns_a.ns_b.ns_c`
  fig_args: Optional[Dict[str, Any]] = None,
      kwargs to pass into chart figure
  
  Examples
  --------
  >>> import autogluon.eda.analysis as eda
  >>> import autogluon.eda.visualization as viz
  >>> import autogluon.eda.auto as auto
  >>>
  >>> df_train = ...
  >>> df_test = ...
  >>> predictor = ...
  >>>
  >>> auto.analyze(model=predictor, val_data=df_test, anlz_facets=[
  >>>     eda.model.AutoGluonModelEvaluator(),
  >>> ], viz_facets=[
  >>>     viz.model.ConfusionMatrix(fig_args=dict(figsize=(3,3)), annot_kws={"size": 12}),
  >>> ])
  
  See Also
  --------
  :py:class:`~autogluon.eda.analysis.model.AutoGluonModelEvaluator`
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code: def can_handle(self, state: AnalysisState) -> bool:
      return (
          "model_evaluation" in state
          and "confusion_matrix" in state.model_evaluation
      )
  
imports: from typing import Any, Dict, Optional
  import matplotlib.pyplot
  import pandas
  import seaborn
  from yellowbrick.contrib.wrapper import REGRESSOR, ContribEstimator
  from yellowbrick.regressor import residuals_plot
  from autogluon.core.constants import REGRESSION
  from ..state import AnalysisState
  from .base import AbstractVisualization
  from .jupyter import JupyterMixin
label_desc: Use image node_id 2 for calling the ConfusionMatrix obj's underlying member method code with example usage: obj.can_handle(state) and returns: unknown