Computational Thinking, Linear Algebra, Linear Regression, Python, Jupyter Notebook.
at the mean squared norm (L2) and mean absolute error (L1). def regr_metrics(act, pred): return (math.sqrt(metrics.mean_squared_error(act, pred)), metrics.mean_absolute_error(act, pred)) regr_metrics(y_test, regr.predict(test)) (75.36166834955054, 60.629082113104403) Polynomial Features trn.shape