Computational Thinking, Linear Algebra, Linear Regression, Python, Jupyter Notebook.
normalize=False) regr_metrics(y_test, regr.predict(poly.fit_transform(test))) (55.747345922929185, 42.836164292252235) Time is squared in #features and linear in #points, so this will get very slow! %timeit poly.fit_transform(trn) 635 µs ± 9.25 µs per loop (mean ± std. dev. of 7 runs, 1000 loops