Pergunta

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?

enter image description here

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!

Nenhuma solução correta

Licenciado em: CC-BY-SA com atribuição
scroll top