Transforms

Pre-processing of data, feature engineering, or augmentation, are fundamental processes in machine learning. AGenC generalizes these processes under the term transforms. This page will guide through the concept of transforms and demonstrate how to use them within AGenC.

Nomenclature

Transforms are a set of operations that modify data. They can include operations such as data normalization, dimensionality reduction, data augmentation, and much more. These transformations are essential for preparing data for machine learning tasks and improving model performance.

In AGenC, we use the generalized term transform for all types of pre-processing of data, feature engineering, and data augmentation, as they all involve the same fundamental concept of transforming data to get a modified dataset.

AGenC provides a flexible and unified interface to apply transforms to data. The framework allows to combine these transforming steps as needed.