class_docstr: Evaluate AutoGluon model performance.
  
  This analysis requires a trained classifier passed in `model` arg and uses 'val_data' dataset to assess model performance.
  
  It is assumed that the validation dataset should follow the same column names seen by the model and has not been used during the training process.
  
  Parameters
  ----------
  model: TabularPredictor, required
      fitted AutoGluon model to analyze
  val_data: pd.DataFrame, required
      validation dataset to use.
      Warning: do not use data used for training as a validation data.
      Predictions on data used by the model during training tend to be optimistic and might not generalize on unseen data.
  normalize : {'true', 'pred', 'all'}, default=None
      Normalizes confusion matrix over the true (rows), predicted (columns)
      conditions or all the population. If None, confusion matrix will not be
      normalized.
      Note: applicable only for binary and multiclass classification; ignored for regression models.
  parent: Optional[AbstractAnalysis], default = None
      parent Analysis
  children: Optional[List[AbstractAnalysis]], default None
      wrapped analyses; these will receive sampled `args` during `fit` call
  
  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.layouts.MarkdownSectionComponent(markdown=f'### Model Prediction for {predictor.label}'),
  >>>     viz.model.ConfusionMatrix(fig_args=dict(figsize=(3,3)), annot_kws={"size": 12}),
  >>>     viz.model.RegressionEvaluation(fig_args=dict(figsize=(6,6)), chart_args=dict(marker='o', scatter_kws={'s':5})),
  >>>     viz.layouts.MarkdownSectionComponent(markdown=f'### Feature Importance for Trained Model'),
  >>>     viz.model.FeatureImportance(show_barplots=True)
  >>> ])
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code: def __init__(
      self,
      normalize: Union[None, str] = None,
      parent: Optional[AbstractAnalysis] = None,
      children: Optional[List[AbstractAnalysis]] = None,
      **kwargs,
  ) -> None:
      super().__init__(parent, children, **kwargs)
      self.normalize = normalize
  
file_path: autogluon/eda/src/autogluon/eda/analysis/model.py
imports: from typing import Any, Dict, List, Optional, Union
  import numpy
  import pandas
  from sklearn.metrics import confusion_matrix
  from autogluon.core.constants import BINARY, MULTICLASS, PROBLEM_TYPES_CLASSIFICATION, PROBLEM_TYPES_REGRESSION, REGRESSION
  from autogluon.tabular import TabularPredictor
  from .base import AbstractAnalysis, AnalysisState
label_desc: Use image node_id 1 to create a new AutoGluonModelEvaluator object from inherited base classes: AbstractAnalysis with example: obj = AutoGluonModelEvaluator(normalize, parent, children)