5.) A training set has 1250 samples with a single feature x. You use the polynomial_featuresfunction defined below with max_degree=3 to augment the training set, and definea LinearRegression model as m = LinearRegression( fit_intercept=False ). You call thefit function on m using the augmented training set and corresponding labels. Which of thefollowing 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) 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.

Answered: 5.) A training set has 1250 samples…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

Section: Chapter Questions

Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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