Tabular Data Augmentation
Tabular augmentation supplements tabular data with additional information to make it more useful for predictive analytics. Database, spreadsheet, and table data are examples of tabular data. It involves transforming otherwise insufficient datasets into robust inputs for ML. Tabular augmentation can help turn unstructured data into structured data and can also assist in combining multiple data sources into a single dataset. It is an essential step in data pre-processing for increasing AI predictive accuracy.
The idea of tabular augmentation is to include additional information to a given dataset that can then be used to generate valuable insights. These datasets can come from various sources, such as customer feedback, social media posts, and IoT device logs. Tabular augmentation can add new information columns to the dataset by enriching the existing columns with more informative tags. It increases the completeness of the dataset and provides more accurate...
