Alinäytteistämistä

Alinäytteistämistä, or undersampling, is a technique used in machine learning to address class imbalance problems. When one class in a dataset has significantly more examples than another, it can lead to models that are biased towards the majority class. Undersampling aims to correct this by reducing the number of instances in the majority class.

There are several methods for undersampling. The simplest approach is random undersampling, where a random subset of the majority class instances is removed. More sophisticated methods exist, such as NearMiss, which selects majority class samples based on their distance to minority class samples. Edited Nearest Neighbours (ENN) removes majority class samples whose class label differs from the majority of their k-nearest neighbors. Tomek Links identify pairs of instances from different classes that are very close to each other and remove the majority class instance from such pairs.

The primary benefit of undersampling is that it can help to improve the performance of classification models, particularly in detecting the minority class, which is often the class of interest (e.g., fraud detection, rare disease diagnosis). However, a significant drawback is the potential loss of valuable information contained in the discarded majority class samples. This can sometimes lead to a decrease in the overall accuracy of the model if important patterns are removed. Therefore, the choice of undersampling technique and the extent of undersampling should be carefully considered based on the specific dataset and problem.