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2. Using Scikit-learn fit a linear regression model on the test dataset and predict on the testing dataset. Compare the model’s prediction to the ground truth testing data by plotting the prediction as a line and the ground truth as data points on the same graph. Examine the coef_ and intercept_ attributes of the trained model, what do the values mean?   Note: Linear Regression Reference: https://scikit-learn.org/stable/modules/linear_model.html

2. Using Scikit-learn fit a linear regression model on the test dataset and predict on the testing dataset. Compare the model’s prediction to the ground truth testing data by plotting the prediction as a line and the ground truth as data points on the same graph. Examine the coef_ and intercept_ attributes of the trained model, what do the values mean?   Note: Linear Regression Reference: https://scikit-learn.org/stable/modules/linear_model.html

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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2. Using Scikit-learn fit a linear regression model on the test dataset and predict on the testing dataset. Compare the model’s prediction to the ground truth testing data by plotting the prediction as a line and the ground truth as data points on the same graph. Examine the coef_ and intercept_ attributes of the trained model, what do the values mean?

 

Note:

  • Linear Regression Reference:

https://scikit-learn.org/stable/modules/linear_model.html

               

>>> from sklearn import linear model >>> reg linear >>> reg.fil.([[o1, [11, 1211, 10, 2, 411 >>> reg.predict ([[5], [7], [9]]) array([10., 14., 18.]) = model.LinearRegression ()
Transcribed Image Text:>>> from sklearn import linear model >>> reg linear >>> reg.fil.([[o1, [11, 1211, 10, 2, 411 >>> reg.predict ([[5], [7], [9]]) array([10., 14., 18.]) = model.LinearRegression ()
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