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)
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)
def __init__(self, fit_intercept=True, normalize=False, copy_X=True, n_jobs=1, delta=0.0001, max_iter=10, quantile=0.5, verbose=False): """ Parameters ---------- fit_intercept: boolean, optional, default True whether to calculate the intercept for this model. If set to False, no intercept will be used in calculations (e.g. data is expected to be already centered). normalize: boolean, optional, default False This parameter is ignored when ``fit_intercept`` is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please use :class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on an estimator with ``normalize=False``. copy_X: boolean, optional, default True If True, X will be copied; else, it may be overwritten. n_jobs: int, optional, default 1 The number of jobs to use for the computation. If -1 all CPUs are used. This will only provide speedup for n_targets > 1 and sufficient large problems. max_iter: int, optional, default 1 The number of iteration to do at training time. This parameter is specific to the quantile regression. delta: float, optional, default 0.0001 Used to ensure matrices has an inverse (*M + delta*I*). quantile: float, by default 0.5, determines which quantile to use to estimate the regression. verbose: bool, optional, default False Prints error at each iteration of the optimisation. """ LinearRegression.__init__(self, fit_intercept=fit_intercept, normalize=normalize, copy_X=copy_X, n_jobs=n_jobs) self.max_iter = max_iter self.verbose = verbose self.delta = delta self.quantile = quantile
def __init__(self, args, fit_intercept=True, normalize=False, copy_X=True, n_jobs=None): LinearRegression.__init__(self, fit_intercept=fit_intercept, normalize=normalize, copy_X=copy_X, n_jobs=n_jobs) # set all params not passed to LinReg constructor; created in modeling process (e.g. coef_, intercept_, etc.) for arg, value in args.items(): setattr(self, arg, value)
def __init__(self, degree=2, interaction_only=False, include_bias=True, order='C', normalize=False, copy_X=True, n_jobs=None): PolynomialFeatures.__init__(self, degree=degree, interaction_only=interaction_only, include_bias=include_bias, order=order) LinearRegression.__init__(self, fit_intercept=False, normalize=normalize, copy_X=copy_X, n_jobs=n_jobs)
def __init__(self): LinearRegression.__init__(self)
def __init__(self, subset_size=1): LinearRegression.__init__(self) self.subset_size = subset_size
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)
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)
def __init__(self, degree=1, **kwargs): self.degree = degree LinearRegression.__init__(self, **kwargs)