In binary classification, the threshold is indeed a pivotal aspect. When you’re predicting the probability of an instance belonging to a particular class, a threshold determines at what probability you classify an instance as the positive class (1) or the negative class (0).
Imagine your email service has a spam filter that uses a classification model to decide whether an incoming email is spam or not. The model computes a score (probability) for each email, representing the likelihood it’s spam.