DOC specify type of penalty in ``PoissonRegressor`` and ``TweedieRegressor`` docstring
@@ -496,15 +496,15 @@ class PoissonRegressor(_GeneralizedLinearRegressor):
Parameters
----------
alpha : float, default=1
- Constant that multiplies the penalty term and thus determines the
+ Constant that multiplies the L2 penalty term and determines the
regularization strength. ``alpha = 0`` is equivalent to unpenalized
GLMs. In this case, the design matrix `X` must have full column rank
(no collinearities).
- Values must be in the range `[0.0, inf)`.
+ Values of `alpha` must be in the range `[0.0, inf)`.
fit_intercept : bool, default=True
Specifies if a constant (a.k.a. bias or intercept) should be
- added to the linear predictor (X @ coef + intercept).
+ added to the linear predictor (`X @ coef + intercept`).
solver : {'lbfgs', 'newton-cholesky'}, default='lbfgs'
Algorithm to use in the optimization problem:
@@ -780,15 +780,15 @@ class TweedieRegressor(_GeneralizedLinearRegressor):
For ``0 < power < 1``, no distribution exists.
alpha : float, default=1
- Constant that multiplies the penalty term and thus determines the
+ Constant that multiplies the L2 penalty term and determines the
regularization strength. ``alpha = 0`` is equivalent to unpenalized
GLMs. In this case, the design matrix `X` must have full column rank
(no collinearities).
- Values must be in the range `[0.0, inf)`.
+ Values of `alpha` must be in the range `[0.0, inf)`.
fit_intercept : bool, default=True
Specifies if a constant (a.k.a. bias or intercept) should be
- added to the linear predictor (X @ coef + intercept).
+ added to the linear predictor (`X @ coef + intercept`).
link : {'auto', 'identity', 'log'}, default='auto'
The link function of the GLM, i.e. mapping from linear predictor
