(self, data)
return self.get_model(model, res.actual, probabilities)
Induce a model using the provided `base_learner`, compute probabilities
        on training data and use scipy's `_SigmoidCalibration` or
        `IsotonicRegression` to prepare calibrators.
Induce a model using the provided `base_learner`, compute probabilities
        on training data and use scipy's `_SigmoidCalibration` or
        `IsotonicRegression` to prepare calibrators.
Induce a model using the provided base_learner compute probabilities on training data and use scipy s _SigmoidCalibration or IsotonicRegression to prepare calibrators .
def fit_storage(self, data):
        """
        Induce a model using the provided `base_learner`, compute probabilities
        on training data and use scipy's `_SigmoidCalibration` or
        `IsotonicRegression` to prepare calibrators.
        """
        res = TestOnTrainingData(store_models=True)(data, [self.base_learner])
        model = res.models[0, 0]
        probabilities = res.probabilities[0]
        return self.get_model(model, res.actual, probabilities)
def fit_storage ( self , data ) : res = TestOnTrainingData ( store_models = True ) ( data , [ self . base_learner ] ) model = res . models [ 0 , 0 ] probabilities = res . probabilities [ 0 ] return self . get_model ( model , res . actual , probabilities )