Ridge classification: Interpreting prediction I'm particularly concerned about the following problem when using ridge classification for predicting binary outcome

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*When I'm encoding the binary outcome as 1 and 0; my model accuracy is 0.6456

*When I'm encoding the binary outcome as -1 and 1; my model accuracy is 0.3525

Does anyone know whether the binary outcome in ridge classification is to be made -1 and 1 or 1 and 0 and why?
Any lead is appreciated

Are use sure that when changing the labels you didn't reverse them? Because both results you got are the same, just the second one got the labels in reverse, $1 - 0.65 = 0.35$.
Both encodings are correct since there are variations of logistic loss for both cases, but which one to choose depends on which your software uses.
