Mintakiegyensúlyozás

Mintakiegyensúlyozás, a Hungarian term, translates to sample balancing or sample equilibration. It refers to a set of techniques used in data science and machine learning to address class imbalance issues within a dataset. Class imbalance occurs when the number of instances in one class (the majority class) is significantly higher than the number of instances in another class (the minority class). This can lead to biased models that perform poorly on the minority class, which is often the class of interest, such as detecting rare diseases or fraudulent transactions.

Mintakiegyensúlyozás aims to mitigate this problem by adjusting the distribution of the training data. Common methods include oversampling the minority class, undersampling the majority class, or a combination of both. Oversampling involves creating synthetic or duplicate instances of the minority class to increase its representation. Undersampling, conversely, reduces the number of instances in the majority class. More advanced techniques like SMOTE (Synthetic Minority Over-sampling Technique) generate new synthetic samples for the minority class based on its existing instances. The goal of these balancing methods is to create a more equitable representation of classes, thereby improving the model's ability to learn from and generalize to the minority class. The choice of mintakiegyensúlyozás technique often depends on the specific dataset and the nature of the imbalance.