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Indic Pacific Glossary

Data as Noise

Date of Addition

22 Mar 2025

The concept that data sets contain unwanted, meaningless information (noise) that can interfere with model training and analysis. Noise can manifest as random variations, misclassifications, uncontrolled variables, or superfluous information unrelated to the target phenomenon.


Almost all real-world data sets contain some degree of noise, which can adversely affect the results of data mining analysis and unnecessarily increase storage requirements. Types of noise include random noise (extra information with no correlation to underlying data), misclassified data (incorrectly labeled information), uncontrolled variables (unaccounted factors affecting the data), and superfluous data (completely unrelated information). Techniques for addressing noisy data include filtering (removing unwanted data), data binning (sorting data into categories to reduce variance), and linear regression (determining correlations between variables). Machine learning algorithms can be particularly susceptible to noise, potentially leading to "garbage in, garbage out" scenarios if data quality is poor.

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