stimated coefficients for the linear predictor (`X @ coef_ +
    intercept_`) in the GLM.

intercept_ : float
    Intercept (a.k.a. bias) added to linear predictor.

n_iter_ : int
    Actual number of iterations used in the solver.

_base_loss : BaseLoss, default=HalfSquaredError()
    This is set during fit via `self._get_loss()`.
    A `_base_loss` contains a specific loss function as well as the link
    function. The loss to be minimized specifies the distributional assumption of
    the GLM, i.e. the distribution from the EDM. Here are some examples:

    =======================  ========  ==========================
    _base_loss               Link      Target Domain
    =======================  ========  ==========================
    HalfSquaredError         identity  y any real number
    HalfPoissonLoss          log       0 <= y
    HalfGammaLoss            log       0 < y
    HalfTweedieLoss          log       dependent on tweedie power
    HalfTweedieLossIdentity  identity  dependent on tweedie power
    =======================  ========  ==========================

    The link function of the GLM, i.e. mapping from linear predictor
    `X @ coeff + intercept` to prediction `y_pred`. For instance, with a log link,
    we have `y_pred = exp(X @ coeff + intercept)`.
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