trainingDataSummary

In data science and machine learning, a trainingDataSummary is a concise description of the dataset used to train a model. It documents the data scope, sources, and key characteristics to aid reproducibility and governance.

Contents typically include dataset size (number of samples and rows), feature list and data types, target variables, and the time period or domain covered. It notes data sources (internal records, public datasets, or synthetic data) and any sampling or stratification techniques applied.

Preprocessing and feature engineering are summarized, including missing value handling, normalization, encoding schemes, feature scaling, and any reduction or selection methods. The summary may also mention data splits (training, validation, test) and leakage prevention steps.

Quality and bias considerations are addressed, such as data quality metrics, representativeness of the dataset, checks for leakage, and known limitations. It may describe fairness considerations and measures taken to mitigate bias.

Governance and provenance details are included, covering data versioning, lineage, licensing, privacy protections, retention, and accessibility. A trainingDataSummary supports audits, reproducibility, and compliance by documenting the dataset's characteristics and handling throughout the model lifecycle.