statsmodels.discrete.discrete_model.LogitResults.predict
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LogitResults.predict(exog=None, transform=True, *args, **kwargs)
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Call self.model.predict with self.params as the first argument.
Parameters: exog : array-like, optional
The values for which you want to predict.
transform : bool, optional
If the model was fit via a formula, do you want to pass exog through the formula. Default is True. E.g., if you fit a model y ~ log(x1) + log(x2), and transform is True, then you can pass a data structure that contains x1 and x2 in their original form. Otherwise, you?d need to log the data first.
args, kwargs : :
Some models can take additional arguments or keywords, see the predict method of the model for the details.
Returns: prediction : ndarray or pandas.Series
See self.model.predict
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