label noise
**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.