Data Science, Linear Regression, Machine Learning, Python, Numpy.
the true values of y. Returns: betas: A numpy array of shape (1, n+1) containing the optimized values of the betas. """ X, betas = self.initialize_betas(X) for iteration in range(self.iterations+1): yhat = self.yhat(X, betas) loss = self.loss(yhat, y) if iteration % 1000 == 0: print(loss) if