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 on, often achieving very low error rates. However, when presented with new, unseen data, its performance significantly degrades. This is because the model has become too specific to the training examples and has failed to generalize the underlying principles. It has learned the quirks of the training set rather than the generalizable rules.

Identifying and mitigating ylisovittumisen is a crucial aspect of model building. Techniques such as cross-validation, regularization, and using simpler models are commonly employed to prevent or reduce its occurrence. The goal is to strike a balance between a model that is complex enough to capture the important patterns and simple enough to avoid memorizing noise, thereby ensuring good performance on new data.