Can LogisticRegression be optimised? I am using a LR model and its got 80% prediction accuracy on test data. For the 20% where it has predicted wrongly, I know the right answer of course. I wonder if there is some optimisation method where I can take the trained LR model and iterate the weights inside the model until the 20% failures are 10% for example. I could use an Evolutionary Strategy maybe. Has anybody done that with success or would this be a bad idea because it would lead to an overfit model?

I tried what I suggested and got a small improvement in accuracy but not much, less than 0.5 percent. This was with using hillclimbing to improve the weights from the model. I am trying with genetic algorithms next. I am using binary classification with a balanced dataset so threshhold 0.5 should be good, as far as i know.
My data is balanced, so accuracy is a good measure of model quality, as far as i know.
I am using Python sklearn implementation of LR , by the way. I already optimised the two parameters "C" and "penalty".

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*As noticed by others, accuracy is not the best metric for judging quality of the model.

*Logistic regression predicts probabilities, so to calculate accuracy you must have used some threshold for making hard classifications. If you used the "default" $p>0.5$, it is not necessarily an optimal choice. There are many methods for picking the threshold. This is something you could tune.

*As about the parameters of the model, you already found the optimal parameters given the data, model, and the hyperparameters, so there is nothing to improve further. If the results are not satisfactory, you can tune the hyperparameters, or try different model (either using another model type altogether, such as random-forest, or attempting further refinement of logistic regression, perhaps using a richer set of features).

