class_docstr: Class for all standard transformation steps.
class_docstr_tok: ["Class","for","all","standard","transformation","steps","."]
docstr: Balance the classes in the target column.
docstr_tok: ["Balance","the","classes","in","the","target","column","."]
code: def _balance(self, fix_imbalance_method, session_id):
      """Balance the classes in the target column."""
      self.logger.info("Set up imbalanced handling.")
  
      strategies = dict(
          # clustercentroids=ClusterCentroids,  # Has no sample_indices_
          condensednearestneighbour=CondensedNearestNeighbour,
          editednearestneighborus=EditedNearestNeighbours,
          repeatededitednearestneighbours=RepeatedEditedNearestNeighbours,
          allknn=AllKNN,
          instancehardnessthreshold=InstanceHardnessThreshold,
          nearmiss=NearMiss,
          neighbourhoodcleaningrule=NeighbourhoodCleaningRule,
          onesidedselection=OneSidedSelection,
          randomundersampler=RandomUnderSampler,
          tomeklinks=TomekLinks,
          randomoversampler=RandomOverSampler,
          smote=SMOTE,
          smotenc=SMOTENC,
          smoten=SMOTEN,
          adasyn=ADASYN,
          borderlinesmote=BorderlineSMOTE,
          kmeanssmote=KMeansSMOTE,
          svmsmote=SVMSMOTE,
          smoteenn=SMOTEENN,
          smotetomek=SMOTETomek,
      )
  
      if isinstance(fix_imbalance_method, str):
          fix_imbalance_method = fix_imbalance_method.lower()
          if fix_imbalance_method not in strategies:
              raise ValueError(
                  "Invalid value for the strategy parameter, got "
                  f"{fix_imbalance_method}. Choose from: {', '.join(strategies)}."
              )
          try:
              balance_estimator = FixImbalancer(
                  strategies[fix_imbalance_method](
                      random_state=session_id
                  )
              )
          except TypeError:
              balance_estimator = FixImbalancer(
                  strategies[fix_imbalance_method]()
              )
      elif not hasattr(fix_imbalance_method, "fit_resample"):
          raise TypeError(
              "Invalid value for the fix_imbalance_method parameter. "
              "The provided value must be a imblearn estimator, got "
              f"{fix_imbalance_method.__class__.__name_}."
          )
      else:
          balance_estimator = FixImbalancer(fix_imbalance_method)
  
      self.pipeline.steps.append(
          ("balance", TransformerWrapper(balance_estimator))
      )
  
imports: from copy import deepcopy
  import numpy
  import pandas
  from category_encoders.basen import BaseNEncoder
  from category_encoders.one_hot import OneHotEncoder
  from category_encoders.ordinal import OrdinalEncoder
  from category_encoders.target_encoder import TargetEncoder
  from imblearn.combine import SMOTEENN, SMOTETomek
  from imblearn.over_sampling import ADASYN, SMOTE, SMOTEN, SMOTENC, SVMSMOTE, BorderlineSMOTE, KMeansSMOTE, RandomOverSampler
  from imblearn.under_sampling import AllKNN, CondensedNearestNeighbour, EditedNearestNeighbours, InstanceHardnessThreshold, NearMiss, NeighbourhoodCleaningRule, OneSidedSelection, RandomUnderSampler, RepeatedEditedNearestNeighbours, TomekLinks
  from sklearn.decomposition import PCA, IncrementalPCA, KernelPCA
  from sklearn.feature_selection import SelectFromModel, SelectKBest, SequentialFeatureSelector, VarianceThreshold, f_classif, f_regression
  from sklearn.impute import KNNImputer, SimpleImputer
  from sklearn.model_selection import GroupKFold, KFold, StratifiedKFold, TimeSeriesSplit, train_test_split
  from sklearn.preprocessing import KBinsDiscretizer, LabelEncoder, MaxAbsScaler, MinMaxScaler, PolynomialFeatures, PowerTransformer, QuantileTransformer, RobustScaler, StandardScaler
  from pycaret.containers.models.classification import get_all_model_containers
  from pycaret.containers.models.regression import get_all_model_containers
  from pycaret.internal.preprocess.iterative_imputer import IterativeImputer
  from pycaret.internal.preprocess.transformers import CleanColumnNames, DropImputer, EmbedTextFeatures, ExtractDateTimeFeatures, FixImbalancer, GroupFeatures, RareCategoryGrouping, RemoveMulticollinearity, RemoveOutliers, TargetTransformer, TransformerWrapper, TransformerWrapperWithInverse
  from pycaret.utils.constants import SEQUENCE
  from pycaret.utils.generic import MLUsecase, check_features_exist, df_shrink_dtypes, get_columns_to_stratify_by, normalize_custom_transformers, to_df, to_series
label_desc: Use image node_id 20 for calling the Preprocessor obj's underlying member method code with example usage: obj._balance(fix_imbalance_method, session_id) without return types