DOC Follow doc guideline for LinearRegression docstring
@@ -382,12 +382,12 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):
Parameters
----------
- fit_intercept : bool, optional, default True
+ fit_intercept : bool, default=True
Whether to calculate the intercept for this model. If set
to False, no intercept will be used in calculations
(i.e. data is expected to be centered).
- normalize : bool, optional, default False
+ normalize : bool, 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.
@@ -395,10 +395,10 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):
:class:`sklearn.preprocessing.StandardScaler` before calling ``fit`` on
an estimator with ``normalize=False``.
- copy_X : bool, optional, default True
+ copy_X : bool, default=True
If True, X will be copied; else, it may be overwritten.
- n_jobs : int or None, optional (default=None)
+ n_jobs : int, default=None
The number of jobs to use for the computation. This will only provide
speedup for n_targets > 1 and sufficient large problems.
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
@@ -419,7 +419,7 @@ class LinearRegression(MultiOutputMixin, RegressorMixin, LinearModel):
singular_ : array of shape (min(X, y),)
Singular values of `X`. Only available when `X` is dense.
- intercept_ : float or array of shape of (n_targets,)
+ intercept_ : float or array of shape (n_targets,)
Independent term in the linear model. Set to 0.0 if
`fit_intercept = False`.
