I'm using GridSearchCV to tune hyperparameters for a Logistic Regression multiclass model.
I read on Kaggle that you should choose the hyperparameter that results in the lowest discrepancy between the CV-score and the training score, but in this case this leads to a very low score.
How should I choose the proper C value to ensure generalisability of the model but also high model performance based on the CV-curve below?
From my understanding opting for low discrepency between the two scores ensures the ability of the model to be generalised to unseen data. But on the other hand I want a score as high as possible on unseen data.
Thanks for any help!