I hope not. And not accoring to the docs...
https://github.com/scikit-learn/scikit-learn/blob/ab93d65/sklearn/linear_model/logistic.py#L947
class_weight : dict or 'balanced', optional
Weights associated with classes in the form ``{class_label: weight}``.
If not given, all classes are supposed to have weight one.
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``.
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified.
On Tue, Aug 1, 2017 at 9:03 AM, Johnson, Jeremiah
<jeremiah.john...@unh.edu> wrote:
> Hello all,
>
> I’m looking for confirmation on an implementation detail that is somewhere
> in liblinear, but I haven’t found documentation for yet. When the
> class_weights=‘balanced’ parameter is set in LogisticRegression, then the
> regularisation parameter for an observation from class I is class_weight[I]
> * C, where C is the usual regularization parameter – is this correct?
>
> Thanks,
> Jeremiah
>
>
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