Input Data Testing focuses on the quality of data used for training and prediction in machine learning (ML) models. It involves:
- Verifying data accuracy and completeness
- Checking for biases in training data
- Ensuring data represents real-world scenarios
- Validating data formats and structures
Key Aspects:
- Assessing data quality and relevance
- Identifying and addressing data anomalies
- Ensuring proper data preprocessing
- Verifying data labelling accuracy (for supervised learning)
This testing is crucial for ML model performance and reliability.