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* make raw docstrings where appropriate
* make ExponentialDispersionModel (i.e. TweedieDistribution) pickable:
ExponentialDispersionModel has new properties include_lower_bound,
method in_y_range is not abstract anymore.
* set self.intercept_=0 if fit_intercept=False, such that it is always defined.
* set score to D2, a generalized R2 with deviance instead of squared error,
as does glmnet. This also solves issues with
check_regressors_train(GeneralizedLinearRegressor), which assumes R2 score.
* change of names: weight to weights in ExponentialDispersionModel and to
sample_weight in GeneralizedLinearRegressor
* add class method linear_predictor
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