class_docstr: Class for all standard transformation steps.
class_docstr_tok: ["Class","for","all","standard","transformation","steps","."]
docstr: Scale the features.
docstr_tok: ["Scale","the","features","."]
code: def _normalization(self, normalize_method):
      """Scale the features."""
      self.logger.info("Set up feature normalization.")
  
      norm_dict = {
          "zscore": StandardScaler(),
          "minmax": MinMaxScaler(),
          "maxabs": MaxAbsScaler(),
          "robust": RobustScaler(),
      }
      if normalize_method in norm_dict:
          normalize_estimator = TransformerWrapper(
              norm_dict[normalize_method]
          )
      else:
          raise ValueError(
              "Invalid value for the normalize_method parameter, got "
              f"{normalize_method}. Possible values are: {' '.join(norm_dict)}."
          )
  
      self.pipeline.steps.append(("normalize", normalize_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 22 for calling the Preprocessor obj's underlying member method code with example usage: obj._normalization(normalize_method) without return types