class_docstr: Automagic anytime regressor.
  
  Parameters
  ----------
  refit : boolean, True
      Whether to refit the model on the full dataset (I think).
  
  random_state : random state, int or None (default=None)
      Random state or seed.
  
  verbose : integer, default=1
      Verbosity (higher is more output).
  
  type_hints : dict or None
          If dict, provide type information for columns.
          Keys are column names, values are types as provided by
          detect_types.
  
  shuffle : boolean, default=True
      Whether to shuffle the training set in cross-validation.
docstr: Fit regressor.
  
  Requires to either specify the target as separate 1d array or Series y
  (in scikit-learn fashion) or as column of the dataframe X specified by
  target_col.
  If y is specified, X is assumed not to contain the target.
  
  Parameters
  ----------
  X : DataFrame
      Input features. If target_col is specified, X also includes the
      target.
  y : Series or numpy array, optional.
      Target class labels. You need to specify either y or target_col.
  target_col : string or int, optional
      Column name of target if included in X.
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code: def fit(self, X, y=None, *, target_col=None):
      """Fit regressor.
  
      Requires to either specify the target as separate 1d array or Series y
      (in scikit-learn fashion) or as column of the dataframe X specified by
      target_col.
      If y is specified, X is assumed not to contain the target.
  
      Parameters
      ----------
      X : DataFrame
          Input features. If target_col is specified, X also includes the
          target.
      y : Series or numpy array, optional.
          Target class labels. You need to specify either y or target_col.
      target_col : string or int, optional
          Column name of target if included in X.
  
      """
      self._rank_scoring = "r2"
  
      return self._fit(X=X, y=y, target_col=target_col)
  
imports: from importlib.metadata import version
  import warnings
  import numpy
  import pandas
  import sklearn
  from sklearn.metrics import make_scorer, average_precision_score
  from sklearn.base import BaseEstimator, ClassifierMixin, RegressorMixin
  from sklearn.preprocessing import LabelEncoder
  from sklearn.utils.multiclass import type_of_target
  from sklearn.pipeline import make_pipeline, Pipeline
  from sklearn.exceptions import UndefinedMetricWarning
  from sklearn.model_selection import StratifiedKFold, KFold
  from sklearn.model_selection._validation import _fit_and_score
  from sklearn.utils.validation import check_is_fitted
  from dabl._available_if import available_if
  from sklearn.dummy import DummyClassifier
  from sklearn.experimental import enable_halving_search_cv
  from sklearn.model_selection import HalvingGridSearchCV
  from .preprocessing import EasyPreprocessor, detect_types
  from .pipelines import get_fast_classifiers, get_fast_regressors, get_any_classifiers
  from .utils import _validate_Xyt
label_desc: Use image node_id 4 for calling the SimpleRegressor obj's underlying member method code with example usage: obj.fit(X, y) and returns: self