Alulmintavételezés

Alulmintavételezés is a Hungarian term that translates to "under-sampling" in English and refers to a data preprocessing technique used in machine learning. It is employed when dealing with imbalanced datasets, where the number of instances in one class (the majority class) significantly outweighs the number of instances in another class (the minority class).

The core idea of under-sampling is to reduce the number of instances in the majority class to match the number of instances in the minority class. This is done by randomly discarding some of the data points from the majority class. The goal is to create a more balanced dataset, which can help to prevent machine learning models from becoming biased towards the majority class and improve their performance in classifying the minority class.

There are various under-sampling methods. Simple random under-sampling involves randomly removing instances from the majority class until the desired balance is achieved. More sophisticated methods include NearMiss, which selects majority class instances based on their proximity to minority class instances, and Tomek Links, which identifies pairs of instances from different classes that are very close to each other and removes the majority class instance from such pairs.

While under-sampling can be effective in improving model performance on imbalanced datasets, it has a potential drawback: the loss of potentially valuable information from the discarded majority class instances. This can sometimes lead to a decrease in the overall predictive accuracy of the model. Therefore, the decision to use under-sampling and the specific method chosen should be carefully considered based on the dataset and the problem at hand.