如何在sklearn逻辑回归中选择初始偏差值？

在训练逻辑回归时，它经历一个迭代过程，在每个过程中它计算 x 变量的权重和偏差值以最小化损失函数。

来自官方 sklearn 代码类 LogisticRegression | scikit-learn 中的线性模型，逻辑回归类的拟合方法如下

def fit(self, X, y, sample_weight=None):
    """
    Fit the model according to the given training data.
    Parameters
    ----------
    X : {array-like, sparse matrix} of shape (n_samples, n_features)
        Training vector, where n_samples is the number of samples and
        n_features is the number of features.
    y : array-like of shape (n_samples,)
        Target vector relative to X.
    sample_weight : array-like of shape (n_samples,) default=None
        Array of weights that are assigned to individual samples.
        If not provided, then each sample is given unit weight.
        .. versionadded:: 0.17
           *sample_weight* support to LogisticRegression.

我猜sample_weight = weightx 变量如果没有给出就设置为 1，偏差值也是 1 吗？

回答