Data noise refers to the random errors or variances in measured data that can obscure the true signal or underlying patterns. It can arise from various sources, such as sensor inaccuracies, environmental factors, or inherent variability in the data collection process. In machine learning, data noise can significantly impact model performance, leading to incorrect predictions and reduced reliability.

There are different types of data noise, including Gaussian noise, which follows a normal distribution, and salt-and-pepper noise, where random pixels in an image are altered. Understanding these noise types is crucial for developing effective strategies to mitigate their effects on machine learning models. For instance, techniques such as data augmentation or noise filtering can help improve model robustness against noisy data ¹.

Addressing data noise is essential, especially in real-world applications where data quality can vary significantly. Researchers are encouraged to incorporate noise considerations into their models to enhance reliability and performance in practical scenarios ¹.