stability or accuracy in making model, which one is more important?

I know that both stability and accuracy of answer are important in making model, but if we just have the option of choosing one of them then which one comes first? By my view, when we have unstable but enough accurate solution ( a solution with oscillation) , always we can get right answer by mean of unstable values. But when our model answer converges to inaccurate answer then there is no opportunity to find the right answer.

Answer 1

[Sabin]

The importance of stability versus accuracy in model development depends on the specific context and goals of the project. Accuracy refers to how close the model's predictions are to the actual outcomes. Focusing solely on accuracy can sometimes lead to overfitting, where the model performs well on training data but poorly on unseen data. Stability involves the model's robustness and consistency across different datasets or under various conditions. Ultimately, the choice between prioritizing stability or accuracy should be guided by the specific needs and constraints of the application.