ylisovittumisen
Ylisovittumisen, a Finnish term, translates roughly to "over-fitting" in English and refers to a concept primarily encountered in the field of statistics and machine learning. It describes a situation where a statistical model, or a machine learning algorithm, learns the training data too well. This means the model not only captures the underlying patterns and relationships in the data but also begins to memorize the noise or random fluctuations present in that specific dataset.
The consequence of ylisovittumisen is that the model performs exceptionally well on the data it was trained
Identifying and mitigating ylisovittumisen is a crucial aspect of model building. Techniques such as cross-validation, regularization,