üldistamisvõime

Üldistamisvõime, commonly translated as generalization ability, refers to the capacity of a model or system to perform well on new, unseen data after being trained on a specific dataset. This is a fundamental concept in machine learning and artificial intelligence. A model with good generalization ability has learned the underlying patterns and structures in the training data rather than simply memorizing it. This allows it to adapt to variations and make accurate predictions or decisions in real-world scenarios.

Overfitting is a major challenge related to generalization ability. It occurs when a model is too complex for the amount of training data available, leading it to perform exceptionally well on the training set but poorly on new data. Conversely, underfitting happens when a model is too simple to capture the essential patterns in the data, resulting in poor performance on both training and unseen data.

Techniques such as cross-validation, regularization, and using larger or more diverse datasets are employed to improve generalization ability. The ultimate goal in developing predictive models is to achieve a balance between fitting the training data and generalizing to new instances. A model that generalizes well is considered robust and reliable.