What would be a decent threshold for classification problem?
There is no universal answer.  Instead, it depends on your application.  What counts as useful for your application?  That determines what should count as a useful or good-enough machine learning algorithm.  What counts as useful will vary widely from application to application; some applications require 99.99% accuracy, others might be happy with 52% accuracy.

In practice, there are also multiple ways to define accuracy.  If you expect that (in the ground truth) half of the objects should be classified 'good' and half as 'bad', then the obvious accuracy measure suffices, i.e., counting what fraction of instances your classifier outputs the correct answer.  However, if 'good' objects are a lot more common than 'bad' objects, or if the penalty for mis-classifying a 'good' object as 'bad' is very different from the penalty for mis-classifying a 'bad' object as 'good', then you might want to measure accuracy differently.  The best measure of accuracy is again application-dependent; there is no single universal answer.