class_docstr: Automagic anytime classifier.
  
  Parameters
  ----------
  refit : boolean, True
      Whether to refit the model on the full dataset.
  
  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.
  
  Attributes
  ----------
  est_ : sklearn estimator
      Best estimator found.
code: def _preprocess_target(self, y):
      target_type = type_of_target(y)
      le = LabelEncoder().fit(y)
      y = pd.Series(y)
      self.classes_ = le.classes_
  
      if target_type == "binary":
          minority_class = y.value_counts().index[1]
          my_average_precision_scorer = make_scorer(
              average_precision_score,
              pos_label=minority_class,
              needs_threshold=True,
          )
          scoring = {
              "accuracy": "accuracy",
              "average_precision": my_average_precision_scorer,
              "roc_auc": "roc_auc",
              "recall_macro": "recall_macro",
              "f1_macro": "f1_macro",
          }
      elif target_type == "multiclass":
          scoring = [
              "accuracy",
              "recall_macro",
              "precision_macro",
              "f1_macro",
          ]
      else:
          raise ValueError(
              "Unknown target type: {}".format(target_type)
          )
      return y, scoring
  
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 3 for calling the SimpleClassifier obj's underlying member method code with example usage: obj._preprocess_target(y) and returns: y,  scoring