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 specific examples it was trained on. This means that while it may achieve very high accuracy on the training dataset, its ability to generalize to new data is significantly diminished. It has memorized the training examples rather than learning the underlying patterns or relationships that would be applicable more broadly.

The consequences of overfitting include poor predictive accuracy on test or real-world data. This is a common challenge in model development, and various techniques are employed to mitigate it. These techniques often involve simplifying the model, using more training data, or applying regularization methods that penalize model complexity. Regularization, for example, adds a penalty term to the model's objective function, discouraging overly complex solutions. Cross-validation is another crucial technique used to assess a model's performance on unseen data and detect overfitting. By systematically evaluating the model on different subsets of the data, one can gain a more robust understanding of its generalization capabilities.