Data Quality for Machine Learning
Definition
Data quality for machine learning refers to the assessment and assurance that training data meets the standards of accuracy, completeness, consistency, and relevance required for a model to learn effectively and generalize correctly.
Purpose
The purpose of data quality for machine learning is to prevent garbage-in-garbage-out scenarios by ensuring that the data used to train models accurately represents the problem domain and does not introduce systematic errors or biases.
Key Characteristics
- Accuracy verification against ground truth or expert validation
- Completeness assessment for missing values and coverage gaps
- Consistency checking across data sources and time periods
- Relevance evaluation for alignment with model objectives
- Timeliness assessment for currency of data relative to deployment context
Usage in Practice
In practice, data quality for machine learning is assessed before training to identify and remediate data issues, during training to detect anomalies, and in production to monitor for data drift that could degrade model performance.
One implementation of this concept is offered by Kenaz through the AI Data Preparation service.