Timeline for How to implement Adaptive Lasso penalty for a Logistic regression in Python?
Current License: CC BY-SA 4.0
5 events
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| Dec 20, 2022 at 10:28 | vote | accept | Jim R. | ||
| Nov 10, 2022 at 15:23 | history | edited | Richard Hardy | CC BY-SA 4.0 |
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| Nov 10, 2022 at 15:10 | comment | added | John Madden | (+1) So just to make things extra straightforward, in python this approach could be implemented by using LogisticRegression with the inverse regularization strength C=10000 or some other big number. Then, take each column of your data and divide it by $|\hat{\beta}_j|^\gamma$ where $\beta_j$ comes from the _coef field of the LogisticRegression object and $\gamma$ is a positive hyperparameter; perhaps $\gamma=1$. Finally, create a second LogisticRegression object using the transformed X variables and a more reasonable C (chosen in the usual manner(s)). | |
| Nov 10, 2022 at 14:40 | history | edited | Richard Hardy | CC BY-SA 4.0 |
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| Nov 10, 2022 at 12:04 | history | answered | Richard Hardy | CC BY-SA 4.0 |