A Universal Metric for Robust Evaluation of Synthetic Tabular Data

Abstract

Synthetic tabular data generation becomes crucial when real data are limited, expensive to collect, or simply cannot be used due to privacy concerns. However, producing good quality synthetic data is challenging. Several probabilistic, statistical, generative adversarial networks and variational autoencoder-based approaches have been presented for synthetic tabular data generation. Once generated, evaluating the quality of the synthetic data is quite challenging. Some of the traditional metrics have been used in the literature, but there is lack of a common, robust, and single metric. This makes it difficult to properly compare the effectiveness of different synthetic tabular data generation methods. In this article, we propose a new universal metric, TabSynDex, for the robust evaluation of synthetic data. The proposed metric assesses the similarity of synthetic data with real data through different component scores, which evaluate the characteristics that are desirable for “high-quality” synthetic data. Being a single score metric and having an implicit bound, TabSynDex can also be used to observe and evaluate the training of neural network-based approaches. This would help in obtaining insights that was not possible earlier. We present several baseline models for comparative analysis of the proposed evaluation metric with existing generative models. We also give a comparative analysis between TabSynDex and existing synthetic tabular data evaluation metrics. This shows the effectiveness and universality of our metric over the existing metrics.