NearMiss is an undersampling technique that can be used to handle imbalanced data. In many real-world applications, datasets are often imbalanced, where the number of samples in one class is significantly higher than the other. This can lead to biased models that perform poorly on the minority class.
To address this issue, various techniques have been developed to balance the class distribution, one of which is undersampling. Undersampling involves reducing the number of samples in the majority class, which can balance the class distribution and improve model performance. NearMiss is one such undersampling technique that selects samples from the majority class that are closest to the minority class, effectively reducing the overlap between the two classes.
NearMiss is a powerful technique that can significantly improve the performance of models trained on imbalanced data. It is easy to implement and can be used in combination with other techniques such as oversampling to further improve model performance. In the following article, we will explore NearMiss in more detail, discuss its advantages and limitations, and provide examples of how it can be used in real-world applications.
Imbalanced Data
Imbalanced data is a common problem in machine learning, where the distribution of classes in the dataset is not uniform. In some cases, one class may be significantly more prevalent than the others. This can lead to poor performance of machine learning models, as they tend to favor the majority class and ignore the minority class.
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