Label noise refers to errors or inaccuracies in the target labels of a training dataset — situations where the assigned label doesn't correctly represent the true category or value of an example. It is one of the most pervasive data quality issues in machine learning.

Sources of Label Noise

• Annotator Errors: Human mistakes due to fatigue, carelessness, or misunderstanding of guidelines.
• Ambiguous Examples: Genuinely borderline cases where the "correct" label is debatable.
• Automatic Labeling: Heuristic or programmatic labeling (distant supervision, regex rules) introduces systematic errors.
• Data Entry Errors: Typos, mislabeled files, or data pipeline bugs.
• Temporal Drift: Labels that were correct at annotation time may become incorrect as the world changes.

Types of Label Noise

• Uniform (Random) Noise: Any example has an equal chance of being mislabeled. Each class is equally likely to be confused with any other.
• Class-Dependent Noise: Certain classes are more likely to be confused with each other (e.g., "neutral" vs. "slightly positive" sentiment).
• Instance-Dependent Noise: Noise probability depends on the features of the example — harder examples near decision boundaries are more likely to be mislabeled.

Impact on Models

• Reduced Accuracy: Models trained on noisy labels learn incorrect patterns, degrading test performance.
• Memorization: Deep neural networks can perfectly memorize noisy labels during training, hurting generalization.
• Biased Decision Boundaries: Systematic noise (e.g., always confusing class A with class B) shifts learned boundaries.

Mitigation Strategies

• Data Cleaning: Use tools like Cleanlab or Confident Learning to identify and correct mislabeled examples.
• Robust Training: Loss functions and algorithms designed to be less sensitive to label noise (see noisy labels learning).
• Multi-Annotator: Collect multiple annotations per example and use majority vote or probabilistic aggregation.
• Curriculum Learning: Train on "easy" (likely correct) examples first, then gradually introduce harder ones.

Label noise is estimated to affect 5–40% of labels in typical real-world datasets, making noise-aware practices essential for reliable machine learning.