ülesobrastamine

Ülesobrastamine is an Estonian term that translates to "over-adaptation" or "over-fitting" in English, primarily used in the context of statistics and machine learning. It describes a phenomenon where a model becomes too closely tailored to the specific training data it has been exposed to, to the detriment of its ability to generalize to new, unseen data.

When a model is over-adapted, it has learned not only the underlying patterns and relationships within the data but also the random fluctuations and noise that are unique to that particular dataset. This leads to a situation where the model performs exceptionally well on the training data but poorly on any data it has not encountered before. Essentially, it has memorized the training examples rather than learning the general principles.

The causes of ülesobrastamine can include having a model that is too complex for the amount of training data available, training the model for too long, or using inappropriate feature selection. Identifying and mitigating ülesobrastamine is a crucial step in building effective predictive models. Techniques such as cross-validation, regularization, and early stopping are commonly employed to prevent or reduce its occurrence, ensuring that the model is robust and performs well in real-world applications.