5.) A training set has 1250 samples with a single feature x. You use the polynomial_features function defined below with max_degree=3 to augment the training set, and define a LinearRegression model...

Extracted text: 5.) A training set has 1250 samples with a single feature x. You use the polynomial_features function defined below with max_degree=3 to augment the training set, and define a LinearRegression model as m = LinearRegression( fit_intercept=False ). You call the fit function on m using the augmented training set and corresponding labels. Which of the following correctly represents the model learned by Sci-kit Learn's LinearRegression? def polynomial_features(x, max_degree): return pd.DataFrame( { i: x ** i for i in range(max_degree+1) } ) m.coef_[0] + m.coef_[1]*x + m.coef_[2]*(x**2) + m.coef_[3]*(x**3) + m.coef_[4]*(: m.coef_[0]*x + m.coef_[1]*(x**2) + m.coef-[2]*(x**3) m.coef_[0] + m.coef_[1]*x + m.coef_[2]*(x**2) + m.coef_[3]*(x**3) m.coef_[0]*x + m.coef_[1]*(x**2) + m.coef_[3]*(x**3) + m.coef_[4]*(x**4)


Extracted text: 6.) If a linear regression model fits the data perfectly (the line passes through every point exactly), then this means: O The RSS will be larger than the TSS (computed on the training data) O The RSS will be close to the TSS (computed on the training data) O The RSS on the training data will be zero.