👉 Shadow math is a technique used in machine learning to train models on negative examples or "shadow" data, which are typically not used during the primary training phase. Instead of focusing solely on positive examples, shadow math involves generating and incorporating additional negative samples to improve the model's robustness and generalization. This is particularly useful when the dataset is imbalanced or when the model needs to handle edge cases more effectively. By training on both positive and shadowed (negative or alternative) examples, the model can learn more nuanced decision boundaries and reduce overfitting to the primary training data. This approach helps in creating a more balanced and resilient model, especially in scenarios where the primary dataset lacks sufficient diversity or contains rare but critical cases.
