Timeline for Predicting house price using linear regression
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 28, 2019 at 21:04 | comment | added | Soner from The Ottoman Empire | sir could you glance at the question? --> datascience.stackexchange.com/questions/52789/… | |
| May 23, 2019 at 10:42 | comment | added | Peter | no its in the text just before the output | |
| May 23, 2019 at 10:17 | comment | added | Soner from The Ottoman Empire | thank you very much. Is there mean absolute error output in your code I couldn't see | |
| May 23, 2019 at 9:41 | comment | added | Peter | When you add non-linear features (x^2 or log(x)) you can often achieve a better fit to the data. Recall that you impose a functional form in OLS. It is linear if y = a + b x. However, if your x has a non-linear pattern you can add y = a + b x + c x^2 (or whatever function) to improve the fit. You can play with different functional forms. In my app posted above, I added x+x^2+...+x^10. Its the "poly" thing in R. You really should have a good look in a proper textbook if you are not familiar with this aspects of basic regression. Its important to understand that. | |
| May 23, 2019 at 9:25 | comment | added | Soner from The Ottoman Empire | Very thanks for your recommendations. I dropped down the indicators with no variation but I don't understand why we need to do last two steps, adding polynomials and logarithm. Would you mind explaining? | |
| May 22, 2019 at 13:57 | history | answered | Peter | CC BY-SA 4.0 |