11.3.1.2. astroML.linear_model.PolynomialRegression

class astroML.linear_model.PolynomialRegression(degree=1, interaction_only=False, fit_intercept=True, regularization='none', kwds=None)[source]

Polynomial Regression with errors in y

Parameters
degreeint

degree of the polynomial.

interaction_onlybool (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_interceptbool (optional)

if True (default) then fit the intercept of the data

regularizationstring (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)

Attributes
coef_

Methods

fit

predict
__init__(degree=1, interaction_only=False, fit_intercept=True, regularization='none', kwds=None)[source]

Initialize self. See help(type(self)) for accurate signature.