cbdd0d6690f9bf014ed428022dfda67be4b779f2,costcla/models/regression.py,CostSensitiveLogisticRegression,predict_proba,#CostSensitiveLogisticRegression#Any#,217

Before Change

    def predict_proba(self, x_test):
        //Calculate the prediction of a LogRegression
        if self.intercept == True:
            x_test = np.hstack((np.ones((x_test.shape[0], 1)), x_test))
        p = np.zeros((x_test.shape[0], 2))
        p[:, 1] = self.sigmoid(np.dot(x_test, self.theta))
        p[:, 0] = 1 - p[:, 1]
        return p

    def predict(self, x_test, cut_point=0.5):

After Change

        else:
            self.coef_ = res.x

    def predict_proba(self, X):
        Probability estimates.

        The returned estimates.

        Parameters
        ----------
        X : array-like, shape = [n_samples, n_features]

        Returns
        -------
        T : array-like, shape = [n_samples, 2]
            Returns the probability of the sample for each class in the model.
        
        y_prob = np.zeros((X.shape[0], 2))
        y_prob[:, 1] = _sigmoid(np.dot(X, self.coef_) + self.intercept_)
        y_prob[:, 0] = 1 - y_prob[:, 1]
        return y_prob

    def predict(self, X, cut_point=0.5):

Instances