Klassvikter

Klassvikter, a Swedish term, translates to "class weights" or "class imbalances" in English and is a concept primarily used in statistics and machine learning. It refers to a situation where the number of observations in different classes of a classification problem is not equal. For instance, in a medical diagnosis dataset, the number of patients with a rare disease might be significantly lower than the number of healthy patients. This disparity is known as class imbalance.

Class imbalance can pose significant challenges when training machine learning models. Standard algorithms often perform poorly in such scenarios because they tend to be biased towards the majority class, leading to high overall accuracy but poor performance on the minority class, which is often the class of interest. For example, a model might correctly predict all healthy patients but misclassify all patients with the rare disease, resulting in a high accuracy but failing its intended purpose.

Several techniques exist to address class imbalance. Resampling methods, such as oversampling the minority class or undersampling the majority class, aim to balance the dataset. Another approach involves using algorithms that are inherently less sensitive to class imbalance or modifying existing algorithms to give more weight to misclassifications of the minority class. Cost-sensitive learning is a common technique where different misclassification costs are assigned to different classes, penalizing misclassifications of the minority class more heavily. Choosing the appropriate method depends on the specific dataset and the goals of the classification task.