túlilleszkedéshez

Túlilleszkedéshez is a Hungarian term that translates to "overfitting" in English, a concept primarily used in machine learning and statistics. It describes a situation where a 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.

When a model overfits, it essentially memorizes the training examples rather than generalizing from them. This results in a high accuracy on the data it was trained on but a significantly lower accuracy when applied to different datasets. The model becomes overly complex and sensitive to the specific characteristics of the training set, making it brittle and unreliable for real-world applications.

Several factors can contribute to overfitting, including using a model with too many parameters relative to the amount of training data, training for too long, or having noisy data. Techniques to combat overfitting include cross-validation, regularization methods (like L1 or L2 regularization), early stopping during training, and using more training data. The goal is to find a balance between fitting the training data well enough to capture underlying patterns and avoiding excessive complexity that leads to poor generalization.