class_docstr: This plot shows residuals on the vertical axis vs prediction on horizontal axis.
  
  This visualization depends on :py:class:`~autogluon.eda.analysis.model.AutoGluonModelEvaluator` analysis.
  
  Parameters
  ----------
  residuals_plot_mode: Optional[str], default = 'qoq'
      Additional plot to render to the right of the main plot. The supported values:
      - `qoq` (default) - Q-Q plot, which is a common way to check that residuals are normally distributed. If the residuals are normally distributed,
      then their quantiles when plotted against quantiles of normal distribution should form a straight line.
      - `hist` - display histogram that our error is normally distributed around zero, which also generally indicates a well fitted model
      - any other value - don't render additional details
  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.RegressionEvaluation(fig_args=dict(figsize=(6,6)), marker='o', scatter_kws={'s':5}),
  >>> ])
  
  See Also
  --------
  :py:class:`~autogluon.eda.analysis.model.AutoGluonModelEvaluator`
class_docstr_tok: ["This","plot","shows","residuals","on","the","vertical","axis","vs","prediction","on","horizontal","axis",".","This","visualization","depends","on",":","py",":","class",":","`","~autogluon.eda.analysis.model.AutoGluonModelEvaluator","`","analysis",".","Parameters","--","--","--","--","--","residuals_plot_mode",":","Optional","[","str","]",",","default","=","'qoq'","Additional","plot","to","render","to","the","right","of","the","main","plot",".","The","supported","values",":","-","`","qoq","`","(","default",")","-","Q-Q","plot",",","which","is","a","common","way","to","check","that","residuals","are","normally","distributed",".","If","the","residuals","are","normally","distributed",",","then","their","quantiles","when","plotted","against","quantiles","of","normal","distribution","should","form","a","straight","line",".","-","`","hist","`","-","display","histogram","that","our","error","is","normally","distributed","around","zero",",","which","also","generally","indicates","a","well","fitted","model","-","any","other","value","-","do","n't","render","additional","details","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.RegressionEvaluation","(","fig_args=dict","(","figsize=","(","6,6",")",")",",","marker=","'","o","'",",","scatter_kws=","{","'s':5","}",")",",",">",">",">","]",")","See","Also","--","--","--","--",":","py",":","class",":","`","~autogluon.eda.analysis.model.AutoGluonModelEvaluator","`"]
code: def _render(self, state: AnalysisState) -> None:
      self.render_header_if_needed(state, "Prediction vs Target")
      res_plot_kwargs = self._get_plot_mode()
      fig, ax = plt.subplots(**self.fig_args)
      (
          y_pred_train,
          y_true_train,
          y_pred_test,
          y_true_test,
      ) = RegressionEvaluation._repack_parameters(
          state.model_evaluation
      )
      residuals_plot(
          _YellowbrickAutoGluonWrapper(state.model),
          y_pred_train,
          y_true_train,
          y_pred_test,
          y_true_test,
          show=False,
          ax=ax,
          **res_plot_kwargs,
      )
      plt.show(fig)
  
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 4 for calling the RegressionEvaluation obj's underlying member method code with example usage: obj._render(state) without return types