yleistyskykyyn

Yleistyskyky, often translated as generalization ability, refers to the capacity of an artificial intelligence or a machine learning model to perform well on new, unseen data after being trained on a specific dataset. It is a fundamental concept in machine learning and is crucial for developing models that are useful in real-world applications. A model with good generalization ability can adapt to new situations and data patterns that were not explicitly present in its training set.

Poor generalization ability, also known as overfitting, occurs when a model learns the training data too well, including its noise and specific peculiarities. Consequently, the model performs poorly when presented with data it has not encountered before. This can happen if the model is too complex for the amount of training data available or if the training process is not properly regularized.

Conversely, underfitting occurs when a model is too simple to capture the underlying patterns in the training data, leading to poor performance on both training and unseen data. Achieving good generalization often involves finding a balance between model complexity and the amount and diversity of training data, along with employing appropriate training techniques and regularization methods. Metrics like validation accuracy are used to estimate a model's generalization ability during the development process.