DOC improve `GammaRegressor` docstring
@@ -627,15 +627,15 @@ class GammaRegressor(_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:
@@ -666,7 +666,7 @@ class GammaRegressor(_GeneralizedLinearRegressor):
warm_start : bool, default=False
If set to ``True``, reuse the solution of the previous call to ``fit``
- as initialization for ``coef_`` and ``intercept_`` .
+ as initialization for `coef_` and `intercept_`.
verbose : int, default=0
For the lbfgs solver set verbose to any positive number for verbosity.
@@ -675,7 +675,7 @@ class GammaRegressor(_GeneralizedLinearRegressor):
Attributes
----------
coef_ : array of shape (n_features,)
- Estimated coefficients for the linear predictor (`X * coef_ +
+ Estimated coefficients for the linear predictor (`X @ coef_ +
intercept_`) in the GLM.
intercept_ : float
