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First, perform the following tasks: • Make a linear regression model with all the features in the dataset. Use train_test_split to keep 20% of the data for testing. • Use your model to predict values for test set and print the predictions for the first 10 instances of the test data and compare them with actual values. • Print the coefficient values and their corresponding feature name (e.g. age 43, bmi 200, .) • Note that you can access feature_names from diabetes dataset directly • Calculate training-MSE, testing-MSE, and R-squared value. Compare the two models. Did using all available features improve the performance? In [ ]: # Your code goes here In [ ]: # Your code goes here

First, perform the following tasks: • Make a linear regression model with all the features in the dataset. Use train_test_split to keep 20% of the data for testing. • Use your model to predict values for test set and print the predictions for the first 10 instances of the test data and compare them with actual values. • Print the coefficient values and their corresponding feature name (e.g. age 43, bmi 200, .) • Note that you can access feature_names from diabetes dataset directly • Calculate training-MSE, testing-MSE, and R-squared value. Compare the two models. Did using all available features improve the performance? In [ ]: # Your code goes here In [ ]: # Your code goes here

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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First, perform the following tasks: • Make a linear regression model with all the features in the dataset. Use train_test_split to keep 20% of the data for testing. • Use your model to predict values for test set and print the predictions for the first 10 instances of the test data and compare them with actual values. • Print the coefficient values and their corresponding feature name (e.g. age 43, bmi 200, .) • Note that you can access feature_names from diabetes dataset directly • Calculate training-MSE, testing-MSE, and R-squared value. Compare the two models. Did using all available features improve the performance? In [ ]: # Your code goes here In [ ]: # Your code goes here
Transcribed Image Text:First, perform the following tasks: • Make a linear regression model with all the features in the dataset. Use train_test_split to keep 20% of the data for testing. • Use your model to predict values for test set and print the predictions for the first 10 instances of the test data and compare them with actual values. • Print the coefficient values and their corresponding feature name (e.g. age 43, bmi 200, .) • Note that you can access feature_names from diabetes dataset directly • Calculate training-MSE, testing-MSE, and R-squared value. Compare the two models. Did using all available features improve the performance? In [ ]: # Your code goes here In [ ]: # Your code goes here
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