Noise in machine learning refers to irrelevant or meaningless data or distortions in a dataset. Noise can come from errors in data collection, transmission, or processing and can adversely affect the performance of a machine learning model.
Dealing with Noise in Machine Learning
Effective handling of noise is crucial for building robust machine learning models. Techniques to reduce noise include data cleaning, outlier detection, and the use of noise-robust algorithms. Recognizing and mitigating noise can lead to more accurate models and better generalization to new data.
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