Computational Thinking, Linear Algebra, Linear Regression, Python, Jupyter Notebook.
reg_regr.alpha_ 0.0098199431661591518 regr_metrics(y_test, reg_regr.predict(test_feat)) (50.0982471642817, 40.065199085003101) Noise Now we will add some noise to the data idxs = np.random.randint(0, len(trn), 10) y_trn2 = np.copy(y_trn) y_trn2[idxs] *= 10 # label noise regr =