alinäytteistys

Alinäytteistys, also known as undersampling, is a technique used in statistics and machine learning to address class imbalance in datasets. When one class has significantly fewer samples than another, standard classification models may perform poorly on the minority class, often by simply predicting the majority class for most instances. Alinäytteistys aims to mitigate this by reducing the number of samples in the majority class.

There are several methods for performing alinäytteistys. A common approach is random alinäytteistys, where instances are randomly removed from the majority class until a desired balance is achieved. Another method is informed alinäytteistys, which uses algorithms to strategically select which majority class instances to remove, often prioritizing those that are less informative or redundant. Examples of informed techniques include NearMiss, which selects majority class samples that are closest to minority class samples, 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.

The primary benefit of alinäytteistys is its ability to improve the performance of classification models on minority classes. By reducing the dominance of the majority class, the model is forced to learn more nuanced decision boundaries that better capture the characteristics of the underrepresented class. However, alinäytteistys can also lead to a loss of potentially valuable information from the majority class, which might negatively impact the model's overall accuracy or its ability to generalize to unseen data. It is often used in conjunction with oversampling techniques or as a standalone method depending on the specific problem and dataset.