Timeline for How to interpret all zero coefficients in the results of cv.glmnet?
Current License: CC BY-SA 3.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Feb 26, 2016 at 0:44 | vote | accept | cying Jack | ||
| Nov 21, 2015 at 9:40 | comment | added | Erik | @cyingJack I suppose you could make it informative, by comparing it with the accuracy of the model that just predicts the mean all the time. This would be a baseline performance. I would look at classical indicators like R squared instead. | |
| Nov 21, 2015 at 6:33 | comment | added | cying Jack | In terms of the measure for accuracy, it is indeed problematic in this case where all the coefficients are zero. However, in those more proper cases, which means the number of nonzero-coefficients isn't zero,and the output of the prediction is not the mean of the trained data. In this way, the accuracy may be a reasonable indication to measure prediction ability. Is that right? | |
| Nov 21, 2015 at 6:18 | vote | accept | cying Jack | ||
| Nov 21, 2015 at 6:19 | |||||
| Nov 20, 2015 at 2:24 | comment | added | cying Jack | Good explanations! Is that mean all my input features are useless and there is no linear model between input x and response y under the lasso condition? I'm also curious about how to measure a model's ability to predict. Is there any indicators except mse(defined as mean(y-predicted_y)^2)? Many thanks! | |
| Nov 19, 2015 at 16:28 | history | answered | Erik | CC BY-SA 3.0 |