Model Validation

Model validation is the process of checking whether a predictive model works well on new, unseen data. It involves testing the model on different datasets to ensure it accurately captures the real-world patterns it's meant to learn. This helps prevent overfitting—where a model performs well on training data but poorly on new data—and confirms the model's reliability. Essentially, validation provides confidence that the model's predictions are trustworthy and applicable to real-world situations, ensuring it is effective before being used for decision-making.