Q5) Run the code below to check on the model behavior for different polynomials (2,3, 5,10,20). Comment on the generated figure. In [26]: # Hyperparam initialization eta - 0.25 epochs = 500000 # This will take a while but you can set it to 10000. Poly_degree_values - [2, 3, 5, 10, 20] # Initializing plot plt.title( "Regression Lines for Different polynomials") plt.scatter(data.GranulesDiameter, data.BeachSlope, label='Traning Data') # iterating over different alpha values (This is going to take a while) for i in Poly_degree_values: polynomial_x = GeneratePolynomialFeatures (X, i) thetaInit - np.zeros ( (polynomial_x.shape[1],1)) theta, losses = gradientDescent(polynomial_x, Y, thetaInit, eta, epochs) poly - PolynomialFeatures (i) plot_SimpleNonlinearRegression_line(theta, x, poly) plt.legend(Poly_degree_values) plt.show() Regression Lines for Different polynomials 3 25 10 20 20 15 10 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Q5) Run the code below to check on the model behavior for different polynomials (2,3, 5,10,20). Comment on the generated figure. In [26]: # Hyperparam initialization eta - 0.25 epochs = 500000 # This will take a while but you can set it to 10000. Poly_degree_values - [2, 3, 5, 10, 20] # Initializing plot plt.title( "Regression Lines for Different polynomials") plt.scatter(data.GranulesDiameter, data.BeachSlope, label='Traning Data') # iterating over different alpha values (This is going to take a while) for i in Poly_degree_values: polynomial_x = GeneratePolynomialFeatures (X, i) thetaInit - np.zeros ( (polynomial_x.shape[1],1)) theta, losses = gradientDescent(polynomial_x, Y, thetaInit, eta, epochs) poly - PolynomialFeatures (i) plot_SimpleNonlinearRegression_line(theta, x, poly) plt.legend(Poly_degree_values) plt.show() Regression Lines for Different polynomials 3 25 10 20 20 15 10 0.2 0.3 0.4 0.5 0.6 0.7 0.8
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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Question
![Q5) Run the code below to check on the model behavior for different polynomials (2,3, 5,10,20). Comment on the generated figure.
In [26]: # Hyperparam initialization
eta = 0.25
epochs = 500000 # This will take a while but you can set it to 10000.
Poly_degree_values = [2, 3, 5, 10, 20]
# Initializing plot
plt.title( "Regression Lines for Different polynomials")
plt.scatter(data.GranulesDiameter, data.Beachslope, label='Traning Data')
# iterating over different alpha values (This is going to take a while)
for i in Poly_degree_values:
polynomial x = GeneratePolynomialFeatures (X, i)
thetaInit - np.zeros( (polynomial_x.shape[1],1))
theta, losses = gradientDescent (polynomial_x, Y, thetaInit, eta, epochs)
poly = PolynomialFeatures (i)
plot SimpleNonlinearRegression line (theta, X, poly)
plt.legend(Poly_degree_values)
plt.show()
Regression Lines for Different polynomials
2
25
10
20
20
15
10
0.2
0.3
0.4
0.5
0.6
0.7
0.8
In [271: # Write your response here](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F50dcc76c-8cd4-46ab-8681-5bc2ba1a603e%2F2a7e81b6-41b8-4405-9d08-514369a51621%2Fege70v_processed.png&w=3840&q=75)
Transcribed Image Text:Q5) Run the code below to check on the model behavior for different polynomials (2,3, 5,10,20). Comment on the generated figure.
In [26]: # Hyperparam initialization
eta = 0.25
epochs = 500000 # This will take a while but you can set it to 10000.
Poly_degree_values = [2, 3, 5, 10, 20]
# Initializing plot
plt.title( "Regression Lines for Different polynomials")
plt.scatter(data.GranulesDiameter, data.Beachslope, label='Traning Data')
# iterating over different alpha values (This is going to take a while)
for i in Poly_degree_values:
polynomial x = GeneratePolynomialFeatures (X, i)
thetaInit - np.zeros( (polynomial_x.shape[1],1))
theta, losses = gradientDescent (polynomial_x, Y, thetaInit, eta, epochs)
poly = PolynomialFeatures (i)
plot SimpleNonlinearRegression line (theta, X, poly)
plt.legend(Poly_degree_values)
plt.show()
Regression Lines for Different polynomials
2
25
10
20
20
15
10
0.2
0.3
0.4
0.5
0.6
0.7
0.8
In [271: # Write your response here
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