class_docstr: Basis Function with errors in y
  
  Parameters
  ----------
  basis_func : str or function
      specify the basis function to use.  This should take an input matrix
      of size (n_samples, n_features), along with optional parameters,
      and return a matrix of size (n_samples, n_bases).
  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)
class_docstr_tok: ["Basis","Function","with","errors","in","y","Parameters","--","--","--","--","--","basis_func",":","str","or","function","specify","the","basis","function","to","use",".","This","should","take","an","input","matrix","of","size","(","n_samples",",","n_features",")",",","along","with","optional","parameters",",","and","return","a","matrix","of","size","(","n_samples",",","n_bases",")",".","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",")"]
code: def __init__(
      self,
      basis_func="gaussian",
      fit_intercept=True,
      regularization="none",
      kwds=None,
      **kwargs,
  ):
      self.basis_func = basis_func
      self.kwargs = kwargs
      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 BasisFunctionRegression object from inherited base classes: LinearRegression with example: obj = BasisFunctionRegression(basis_func, fit_intercept, regularization, kwds)