generaliseerumisvõimet

Generaliseerumisvõimet, also known as generalization ability, is a fundamental concept in machine learning and artificial intelligence. It refers to the capacity of a model to perform well on new, unseen data after being trained on a specific dataset. A model with good generalization ability can identify underlying patterns and relationships in the training data that are not specific to that particular set of examples, allowing it to make accurate predictions or classifications on data it has never encountered before.

Conversely, a model that exhibits poor generalization ability is said to be overfitting. Overfitting occurs when a model learns the training data too well, including its noise and specific idiosyncrasies. While it might achieve very high accuracy on the training set, it fails to perform adequately on new data because it has essentially memorized the training examples rather than learning the general rules.

Achieving good generalization is a primary goal in model development. Techniques such as cross-validation, regularization, and using diverse and representative training datasets are employed to mitigate overfitting and enhance a model's generalization ability. The ultimate aim is to build models that are robust and reliable in real-world applications, where encountering novel data is the norm.