Synthetic data for design and evaluation of binary classifiers in the context of Bayesian transfer learning.

ABSTRACT: Transfer learning (TL) techniques can enable effective learning in data scarce domains by allowing one to re-purpose data or scientific knowledge available in relevant source domains for predictive tasks in a target domain of interest. In this Data in Brief article, we present a synthetic dataset for binary classification in the context of Bayesian transfer learning, which can be used for the design and evaluation of TL-based classifiers. For this purpose, we consider numerous combinations of classification settings, based on which we simulate a diverse set of feature-label distributions with varying learning complexity. For each set of model parameters, we provide a pair of target and source datasets that have been jointly sampled from the underlying feature-label distributions in the tar

SUBMITTER: Maddouri O 

PROVIDER: S-EPMC9011006 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature