Term tag: data quality

Active Learning

Active learning lets a model choose which unlabelled examples to get labelled next, hitting target accuracy with far fewer labels than random labelling.

Data Augmentation

Making new training examples by transforming existing ones. Valid only when the label is indifferent to the transform — and it adds variety, not information.

Data Leakage

Data leakage is training a model on information it will not have at prediction time. It pushes scores up, not down, which is why review never catches it.

Data Processing

What happens to raw data before a model can use it: ingestion, validation, transformation, ETL vs ELT, batch vs streaming, and what silently breaks.

Feature Scaling

Feature scaling puts numeric features on comparable ranges so no variable dominates by its units alone — why distance- and gradient-based models need it.

Monitoring

Why a deployed model rots while every dashboard stays green — and what you can actually measure when the true labels arrive weeks late, or never.

Synthetic Data

Data generated by a model instead of collected from the world: what synthetic data is, where it helps, and why recursive use triggers model collapse.