class_docstr: Automated Machine Learning for supervised tasks (binary classification, multiclass classification, regression).
class_docstr_tok: ["Automated","Machine","Learning","for","supervised","tasks","(","binary","classification",",","multiclass","classification",",","regression",")","."]
docstr: Fit the AutoML model.
  
  Arguments:
      X (numpy.ndarray or pandas.DataFrame): Training data
  
      y (numpy.ndarray or pandas.Series): Training targets
  
      sample_weight (numpy.ndarray or pandas.Series): Training sample weights
  
      cv (iterable or list): List or iterable with (train, validation) splits representing array of indices.
          It is used only with custom validation (`validation_strategy={'validation_type': 'custom'}`).
  
      sensitive_features (pandas.Series or pandas.DataFrame): Sensitive features to learn fair models
  
  Returns:
      AutoML object: Returns `self`
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code: def fit(
      self,
      X: Union[numpy.ndarray, pandas.DataFrame],
      y: Union[numpy.ndarray, pandas.Series],
      sample_weight: Optional[
          Union[numpy.ndarray, pandas.Series]
      ] = None,
      cv: Optional[Union[Iterable, List]] = None,
      sensitive_features: Optional[
          Union[numpy.ndarray, pandas.Series, pandas.DataFrame]
      ] = None,
  ):
      """Fit the AutoML model.
  
      Arguments:
          X (numpy.ndarray or pandas.DataFrame): Training data
  
          y (numpy.ndarray or pandas.Series): Training targets
  
          sample_weight (numpy.ndarray or pandas.Series): Training sample weights
  
          cv (iterable or list): List or iterable with (train, validation) splits representing array of indices.
              It is used only with custom validation (`validation_strategy={'validation_type': 'custom'}`).
  
          sensitive_features (pandas.Series or pandas.DataFrame): Sensitive features to learn fair models
  
      Returns:
          AutoML object: Returns `self`
      """
      return self._fit(X, y, sample_weight, cv, sensitive_features)
  
imports: import logging
  import matplotlib
  import warnings
  from collections.abc import Iterable
  from re import U
  from typing import List, Optional, Union
  import numpy
  import pandas
  from typing_extensions import Literal
  from supervised.base_automl import BaseAutoML
  from supervised.utils.config import LOG_LEVEL
label_desc: Use image node_id 2 for calling the AutoML obj's underlying member method code with example usage: obj.fit(X, y, sample_weight, cv, sensitive_features) and returns: self