Timeline for How can we evaluate the predicted values in regression model
Current License: CC BY-SA 3.0
4 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 28, 2014 at 13:14 | vote | accept | user3378649 | ||
| Apr 28, 2014 at 12:28 | comment | added | tomka | That seems right, except that a % interpretation is more difficult in the pseudo-R-squared case. It's okay in the linear case, where the ratio is explained against total variance. | |
| Apr 25, 2014 at 13:16 | comment | added | user3378649 | Correct me if I am wrong. If I understand it very well. R-squared is always between 0 and 100%: - 0% indicates that the model is extremely bad. - 100% indicates that the model explains all the variability of the response data around its mean ==> extremely good. | |
| Apr 25, 2014 at 12:05 | history | answered | tomka | CC BY-SA 3.0 |