túlillesztésének
Túlillesztésének is a Hungarian term that translates to "overfitting" in English, a concept primarily discussed in the fields of machine learning, statistics, and data science. Overfitting occurs when a statistical model learns the training data too well, including its noise and random fluctuations, to the point where it negatively impacts the model's performance on new, unseen data.
Essentially, an overfitted model has developed a level of complexity that is too closely tailored to the
The consequences of overfitting include poor predictive accuracy on test or real-world data. This is a common