Description: Model generalization is a fundamental concept in machine learning that refers to a model’s ability to perform well on unseen data that was not used during training. This capability is crucial, as a model that merely memorizes training data may fail when faced with new or different situations. Generalization implies that the model has learned underlying patterns in the data rather than simply recalling specific examples. To achieve good generalization, it is essential to have a representative training dataset and to apply appropriate validation and hyperparameter tuning techniques. Generalization is commonly measured by evaluating the model’s performance on a test dataset, which should be independent of the training set. A well-generalized model can not only predict accurately on data similar to the training data but can also adapt to variations and new instances, making it more robust and useful in various applications. Therefore, generalization is a key indicator of the effectiveness and applicability of a machine learning model in multiple situations and contexts.
