class_docstr: Defines the ErrorAnalysisManager for discovering errors in a model.
class_docstr_tok: ["Defines","the","ErrorAnalysisManager","for","discovering","errors","in","a","model","."]
docstr: Creates an ErrorAnalysisManager object.
  
  :param model: The model to analyze errors on.
      A model that implements sklearn.predict or sklearn.predict_proba
      or function that accepts a 2d ndarray.
  :type model: object
  :param dataset: The dataset including the label column.
  :type dataset: pandas.DataFrame
  :param target_column: The name of the label column.
  :type target_column: str
  :param classes: Class names as a list of strings.
      The order of the class names should match that of the model
      output.  Only required if analyzing a classifier.
  :type classes: list
  :param categorical_features: The categorical feature names.
  :type categorical_features: list[str]
  :param dropped_features: List of features that are omitted for model
                      training. This includes metadata that is useful
                      for evaluating the model.
  :type dropped_features: Optional[List[str]]
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code: def __init__(
      self,
      model: Any,
      dataset: pd.DataFrame,
      target_column: str,
      classes: Optional[List] = None,
      categorical_features: Optional[List[str]] = None,
      dropped_features: Optional[List[str]] = None,
      model_task: Optional[str] = ModelTask.CLASSIFICATION,
  ):
      """Creates an ErrorAnalysisManager object.
  
      :param model: The model to analyze errors on.
          A model that implements sklearn.predict or sklearn.predict_proba
          or function that accepts a 2d ndarray.
      :type model: object
      :param dataset: The dataset including the label column.
      :type dataset: pandas.DataFrame
      :param target_column: The name of the label column.
      :type target_column: str
      :param classes: Class names as a list of strings.
          The order of the class names should match that of the model
          output.  Only required if analyzing a classifier.
      :type classes: list
      :param categorical_features: The categorical feature names.
      :type categorical_features: list[str]
      :param dropped_features: List of features that are omitted for model
                          training. This includes metadata that is useful
                          for evaluating the model.
      :type dropped_features: Optional[List[str]]
      """
      self._true_y = dataset[target_column]
      self._dataset = dataset.drop(columns=[target_column])
      self._feature_names = list(self._dataset.columns)
      self._model_task = model_task
      self._classes = classes
      self._categorical_features = categorical_features
      self._ea_config_list = []
      self._ea_report_list = []
      wrapper_model = get_wrapped_model(model, dropped_features)
      self._analyzer = ModelAnalyzer(
          wrapper_model,
          self._dataset,
          self._true_y,
          self._feature_names,
          self._categorical_features,
          model_task=self._model_task,
          classes=self._classes,
      )
  
imports: import json
  import warnings
  from pathlib import Path
  from typing import Any, List, Optional
  import jsonschema
  import pandas
  from erroranalysis._internal.constants import metric_to_display_name
  from erroranalysis._internal.error_analyzer import ModelAnalyzer
  from erroranalysis._internal.error_report import as_error_report
  from erroranalysis._internal.error_report import json_converter
  from raiutils.exceptions import UserConfigValidationException
  from raiutils.models import ModelTask
  from responsibleai._config.base_config import BaseConfig
  from responsibleai._interfaces import ErrorAnalysisData
  from responsibleai._internal.constants import ErrorAnalysisManagerKeys
  from responsibleai._internal.constants import FileFormats, ListProperties, ManagerNames
  from responsibleai._tools.shared.state_directory_management import DirectoryManager
  from responsibleai.exceptions import ConfigAndResultMismatchException, DuplicateManagerConfigException
  from responsibleai.managers.base_manager import BaseManager
  from responsibleai.utils import _find_features_having_missing_values, _measure_time
label_desc: Use image node_id 1 to create a new ErrorAnalysisManager object from inherited base classes: BaseManager with example: obj = ErrorAnalysisManager(model, dataset, target_column, classes, categorical_features, dropped_features, model_task)