👉 Non-crossover is a concept in machine learning where the model and data are not directly linked. In other words, the model's parameters do not depend on the input or output of the training dataset. In simpler terms, it means that the model doesn't learn from previous training examples but rather learns from new, unseen data. For example, imagine you have a neural network trained to predict whether an email is spam or not. The model would be non-crossover because it does not learn from
