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
degreedistinct input features (so notx[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
get_params([deep])Get parameters for this estimator.
set_params(**params)Set the parameters of this estimator.
fit
predict