Noisy Data and Label Noise

What is noise?

Noise is defined as an undesirable behavior within data. Additionally, any data that a machine cannot easily understand or correctly interpret is also considered noise. In a dataset, noise can take various forms, including outliers, measurement errors, missing values, and labeling errors. It can distort the statistical properties of the data, introduce inaccuracies, and affect the analysis or training of ML models.

Unreliable data collection tools are a common source of errors in datasets, and these errors can be categorized as noise. Such errors arise from unreliable equipment and can substantially impact the accuracy of ML models.

Create a free account to view this lesson.

By signing up, you agree to Educative's Terms of Service and Privacy Policy