class_docstr: Polynomial Regression with errors in y
  
  Parameters
  ----------
  degree : int
      degree of the polynomial.
  interaction_only : bool (optional)
      If true, only interaction features are produced: features that are
      products of at most ``degree`` *distinct* input features (so not
      ``x[1] ** 2``, ``x[0] * x[2] ** 3``, etc.).
  fit_intercept : bool (optional)
      if True (default) then fit the intercept of the data
  regularization : string (optional)
      ['l1'|'l2'|'none'] Use L1 (Lasso) or L2 (Ridge) regression
  kwds: dict
      additional keyword arguments passed to sklearn estimators:
      LinearRegression, Lasso (L1), or Ridge (L2)
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code: def __init__(
      self,
      degree=1,
      interaction_only=False,
      fit_intercept=True,
      regularization="none",
      kwds=None,
  ):
      self.degree = degree
      self.interaction_only = interaction_only
      LinearRegression.__init__(
          self, fit_intercept, regularization, kwds
      )
  
imports: import numpy
  from sklearn.base import BaseEstimator
  from sklearn.preprocessing import PolynomialFeatures
  from sklearn.linear_model import LinearRegression, Lasso, Ridge
label_desc: Use image node_id 1 to create a new PolynomialRegression object from inherited base classes: LinearRegression with example: obj = PolynomialRegression(degree, interaction_only, fit_intercept, regularization, kwds)