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undersampling
This term is primarily used in technical domains, specifically machine learning and digital signal processing. In data science, it describes a strategic reduction of data to combat class imbalance, ensuring a model does not simply memorize the most frequent outcome. It is often contrasted with oversampling, where the minority class is artificially increased.
In the context of signal processing, the term carries a negative connotation of error or failure. Here, it refers to a technical mistake where the sampling frequency is insufficient to capture the original signal accurately, leading to aliasing—a distortion where high-frequency components are incorrectly mapped to lower frequencies.
Meanings
Examples
We used random undersampling to handle the class imbalance in our fraud detection dataset.
The audio quality is terrible because of severe undersampling during the recording process.
Maybe undersampling the majority class will prevent the model from being biased.
I wonder if undersampling is better than oversampling for this specific project.
Undersampling can lead to a loss of valuable information from the majority class.
Wait, did you check if the signal is suffering from undersampling effects?
The researcher applied undersampling to ensure the training set was perfectly balanced.
Aliasing occurs whenever there is undersampling of a continuous signal.
Aliasing occurs whenever there is undersampling of a continuous signal.