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) O m.coef_[Ø]*x + m.coef_[1]*(x**2) + m.coef_[3]*(x**3) + m.coef_[4]*(x**4)
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) O m.coef_[Ø]*x + m.coef_[1]*(x**2) + m.coef_[3]*(x**3) + m.coef_[4]*(x**4)
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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![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)](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F3725565e-8036-4576-ae04-b92186721767%2Fb40a67ef-7f75-41c2-9d96-e116ca2eb1a8%2Fiem1r7r_processed.png&w=3840&q=75)
Transcribed Image 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)
Transcribed Image 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.
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