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Mean Squared Error (MSE ) is one of the metrices that is used to evaluate the performance of a given model. MSE is very simple to calculate , it represents the difference between the actual and predicted values extracted by squared the average difference over the data set. Simply, the MSE is calculated as follow: ( 1/n) * Σ(Υ - X) MSE = • X array of the actual values • Y array of the predicted values • :: A symbol that means "sum". • n: number of the values Write a python code to calculate the MSE. The python code should: 1/ Create X and Y NumPy arrays. 2/ Create a new NumPy array "Diff" which is the difference between the two arrays X and Y. 3/ Create a new NumPy array "Sqd" which is squared of "Diff" array values (loop is not allowed). 4/ Calculate "sum" which is the summation of the "Sqd" array values (loop is not allowed).

Mean Squared Error (MSE ) is one of the metrices that is used to evaluate the performance of a given model. MSE is very simple to calculate , it represents the difference between the actual and predicted values extracted by squared the average difference over the data set. Simply, the MSE is calculated as follow: ( 1/n) * Σ(Υ - X) MSE = • X array of the actual values • Y array of the predicted values • :: A symbol that means "sum". • n: number of the values Write a python code to calculate the MSE. The python code should: 1/ Create X and Y NumPy arrays. 2/ Create a new NumPy array "Diff" which is the difference between the two arrays X and Y. 3/ Create a new NumPy array "Sqd" which is squared of "Diff" array values (loop is not allowed). 4/ Calculate "sum" which is the summation of the "Sqd" array values (loop is not allowed).

Computer Networking: A Top-Down Approach (7th Edition)
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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Mean Squared Error (MSE ) is one of the metrices that is used to evaluate the performance of a given model. MSE is very simple to calculate, it represents the difference between the actual and predicted values extracted by squared the average difference over the data set. Simply, the MSE is calculated as follow: ( 1/n) * Σ(Υ - x2 MSE = • X array of the actual values • Y array of the predicted values • E: A symbol that means "sum". • n: number of the values Write a python code to calculate the MSE. The python code should: 1/ Create X and Y NumPy arrays. 2/ Create a new NumPy array "Diff" which is the difference between the two arrays X and Y. 3/ Create a new NumPy array "Sqd" which is squared of "Diff" array values (loop is not allowed). 4/ Calculate "sum" which is the summation of the "Sqd" array values (loop is not allowed). 5/ Print the MSE, which is the division of the sum by the number of values in Y. Example: X= [123 3] Y = [504 3] Diff = [4 -2 1 0] Sqd = [16 4 1 0] sum = (16+4+1+0) MSE = sum/4 = 5.25
Transcribed Image Text:Mean Squared Error (MSE ) is one of the metrices that is used to evaluate the performance of a given model. MSE is very simple to calculate, it represents the difference between the actual and predicted values extracted by squared the average difference over the data set. Simply, the MSE is calculated as follow: ( 1/n) * Σ(Υ - x2 MSE = • X array of the actual values • Y array of the predicted values • E: A symbol that means "sum". • n: number of the values Write a python code to calculate the MSE. The python code should: 1/ Create X and Y NumPy arrays. 2/ Create a new NumPy array "Diff" which is the difference between the two arrays X and Y. 3/ Create a new NumPy array "Sqd" which is squared of "Diff" array values (loop is not allowed). 4/ Calculate "sum" which is the summation of the "Sqd" array values (loop is not allowed). 5/ Print the MSE, which is the division of the sum by the number of values in Y. Example: X= [123 3] Y = [504 3] Diff = [4 -2 1 0] Sqd = [16 4 1 0] sum = (16+4+1+0) MSE = sum/4 = 5.25
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