class_docstr: Class for all standard transformation steps.
class_docstr_tok: ["Class","for","all","standard","transformation","steps","."]
docstr: Create polynomial features from the existing ones.
docstr_tok: ["Create","polynomial","features","from","the","existing","ones","."]
code: def _polynomial_features(self, polynomial_degree):
      """Create polynomial features from the existing ones."""
      self.logger.info("Set up polynomial features.")
  
      polynomial = TransformerWrapper(
          transformer=PolynomialFeatures(
              degree=polynomial_degree,
              interaction_only=False,
              include_bias=False,
              order="C",
          ),
      )
  
      self.pipeline.steps.append(("polynomial_features", polynomial))
  
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 14 for calling the Preprocessor obj's underlying member method code with example usage: obj._polynomial_features(polynomial_degree) without return types