8dc523d9bd92011caca608833221916d9e1b8130,Orange/classification/logistic_regression.py,LogisticRegressionLearner,fit,#LogisticRegressionLearner#Any#Any#Any#,48

Before Change

        return cost, grad

    def fit(self, X, Y, W):
        if list(np.unique(Y).astype(int)) != [0, 1]:
            raise ValueError("Logistic regression requires a binary class "
                             "variable")

        if Y.shape[1] > 1:
            raise ValueError("Logistic regression does not support "
                             "multi-label classification")

After Change

            intercept_scaling=self.intercept_scaling,
            random_state=self.random_state
        )
        clsf = lr.fit(X, Y.ravel())

        return LogisticRegressionClassifier(clsf)

Instances