Általánosítóképesség

Általánosítóképesség, often translated 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 unseen data after being trained on a specific dataset. A model with good generalization ability has learned the underlying patterns and relationships in the training data rather than simply memorizing the training examples. This distinction is crucial for building models that are useful in real-world applications, where they will inevitably encounter data that differs from what they were trained on.

Overfitting is a common problem that hinders generalization ability. When a model overfits, it performs exceptionally well on the training data but poorly on new data. This typically happens when a model is too complex for the amount of training data available, leading it to capture noise and specificities of the training set that do not represent the broader data distribution. Conversely, underfitting occurs when a model is too simple to capture the underlying patterns in the training data, resulting in poor performance on both training and unseen data.

Techniques like cross-validation, regularization (e.g., L1 and L2 regularization), dropout, and early stopping are employed to improve a model's generalization ability. These methods aim to strike a balance between fitting the training data and avoiding memorization, thereby ensuring the model can reliably make predictions on new, previously unencountered examples. Evaluating generalization ability typically involves testing the trained model on a separate, held-out test dataset that was not used during the training or validation phases.