Noise models and filtering families

noislearn focuses on classification settings where the training data may contain wrong labels, ambiguous instances, or local inconsistencies.

Main noise families

Family	Main idea	Typical use
Distance-based	Compare each instance with its local neighborhood	Fast baselines and local consistency checks
Classifier-based	Compare observed labels against out-of-fold predictions	Label-noise detection from model disagreement
TabPFN-based	Use TabPFN as the base learner and inspect its local explanations	Strong tabular classifier plus explanation support
CNC-NOS	Combine several filters and a weighted noise score	Higher-level cleaning pipeline

Label noise

Label noise appears when an instance is assigned the wrong class. This is the main target of the filtering algorithms in the repository.

Attribute noise

Attribute noise affects feature values rather than the target label. The current API is primarily centered on class-noise filtering, not on feature corruption repair.

Why filtering can be useful

• It can remove or relabel suspicious samples before training the final classifier.
• It can improve downstream generalization when the training labels are unreliable.
• It can provide a structured report that helps audit what was removed and why.

When each family is useful

• Use distance-based filters when you want a simple local consistency baseline.
• Use classifier-based filters when you want out-of-fold disagreement logic.
• Use TabPFN-based filters when you want stronger tabular predictions and local SHAP reports.
• Use CNC-NOS when you want a higher-level cleaning strategy that combines several filters.