Resolved: Final Exam - Question 5
Dear Professor Manov
I believe in question 5 of the final exam there is a misake finding the Lasso Regression Model RMSE,
The code should be as follows:
print("Lasso Regression Model RMSE is: ", math.sqrt(mean_squared_error(y_test, lasso_reg_y_pred)))
However, You have the ridge_Y_pred in both cases so i believe the result should be :
Best Regards,
Mani
Hi Mani!
Thanks for reaching out!
You are absolutely correct that in the explanation, the parameter for lasso should be lasso_reg_y_pred instead of ridge_reg_y_pred. However, the results I obtained with the required input are indeed 26525 for ridge and 26173 for lasso.
Are you sure you are applying the correct arguments for the RepeatedKFold method, namely 10, 5, and 1? Also, you can check if the cross-validator values you're using correspond to the ones from the task. Either way, feel free to post the rest of your code here.
Thank you again - we will edit the typo in the explanation as soon as possible.
Best,
Ivan
Dear Ivan
Thank you in advance; I checked my code again and still get the same results; here is my code; I'd appreciate it if you could point out where I went wrong:
Best Regards,
Mani
Hi Mani!
The difference in the answer comes from the fact that you've integrated the optimization tolerance parameter with a value of 1 (tol=1) as it was done in the lecture. However, as the task in the exam does not provoke any duality gap issues, we decided not to assign a value to the tol parameter which means it used its default value: tol=0.0001. This is why we didn't designate the necessity of specifying an optimization tolerance in the task.
Hope this helps.
Best,
Ivan
Dear Ivan
So whenever we don't assign a value to the Tol Parameter it is 0.0001 by default.
Thank you, Ivan,
Sincerely yours,
Mani
Hi Mani!
Yes, precisely!
Please feel free to post another question should you encounter other difficulties. Thank you.
Best,
Ivan
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