Ridge Regression is the estimator used in this example. Each color in the left plot represents one different dimension of the coefficient vector, and this is displayed as a function of the regularization parameter. The right plot shows how exact the solution is. This example illustrates how a well defined solution is found by Ridge regression and how regularization affects the coefficients and their values. The plot on the right shows how the difference of the coefficients from the estimator c

class numpy.polynomial.legendre.Legendre(coef, domain=None, window=None)

Series.to_json(path_or_buf=None, orient=None, date_format='epoch', double_precision=10, force_ascii=True, date_unit='ms', default_handler=None

DataFrame.combineMult(other)

load_module(name=None) Concrete implementation of importlib.abc.Loader.load_module() where specifying the name

statsmodels.discrete.discrete_model.Poisson.loglike

output A list of str

ndarray.__ne__ x.__ne__(y) <==> x!=y

statsmodels.sandbox.tsa.fftarma.ArmaFft.spdpoly ArmaFft

class DateField(**kwargs) [source] Default widget: