üldistamisprobleemid
The generalization problem, or üldistamisprobleemid in Estonian, refers to the challenge of creating models or solutions that 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 that perfectly fits the training data but fails to generalize to new data is said to have overfit. Conversely, a model that is too simple and performs poorly on both training and new data is considered to have underfit.
The core of the generalization problem lies in finding a balance between model complexity and the amount
Several techniques are employed to address generalization problems. Data augmentation, which involves creating modified versions of