👉 Validation computing is a critical phase in the machine learning process where a model's performance is evaluated on a subset of data known as the validation set, which is distinct from both the training set and the test set. Unlike the training set, where the model learns by adjusting its parameters to minimize error, and the test set, which provides an unbiased assessment of the model's generalization ability, the validation set is used to tune hyperparameters and prevent overfitting. By comparing the model's predictions on the validation set with its actual performance, data scientists can make informed decisions about model adjustments and ultimately select the best configuration to ensure that the model performs well on unseen data, thereby enhancing its reliability and effectiveness in real-world applications.
