Timeline for Lasso regression prediction on test set is predicting towards the mean of the train set?
Current License: CC BY-SA 4.0
9 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| May 21, 2022 at 14:38 | answer | added | EdM | timeline score: 2 | |
| May 20, 2022 at 16:53 | comment | added | Echo | out of the 2112 , 388 were non zeros . I tried using SVR as well , getting same behaviour. | |
| May 20, 2022 at 12:58 | comment | added | EdM | How many of the original 2112 features were retained with non-zero coefficients in the final LASSO model? Have you considered using ridge regression instead? | |
| May 20, 2022 at 12:53 | comment | added | Echo | The variance explained for the validation set is around 70 percent but falls to almost 5-10 % for the test sets having mean actualage to be around 68% and the corresponding predicted age mean to be around 62.6% | |
| May 20, 2022 at 12:02 | comment | added | Richard Hardy | I wonder if the means you mention can tell much about overfitting vs. underfitting. Variances could be more relevant. | |
| May 20, 2022 at 11:19 | comment | added | Echo | I am trying to get accelerated brain aging so the datasets are comparable in terms of age, sex etc but not on the presence of the disease as such. | |
| May 20, 2022 at 10:56 | comment | added | user2974951 | Maybe, or maybe that is the best that your model can do based on your training data. Are your various test sets comparable to your train set? As in are the participants similar in the various sets? | |
| S May 20, 2022 at 10:40 | review | First questions | |||
| May 20, 2022 at 11:14 | |||||
| S May 20, 2022 at 10:40 | history | asked | Echo | CC BY-SA 4.0 |