Generate Synthetic Data to Build Robust Machine Learning Models in Data Scares Scenario

Author(s): Kuriko Iwai

Originally published on Towards AI.

Explore statistical approaches to transform experts knowledge into data with practical examples

Machine learning models need to be trained on sufficient, high-quality data that will recur in the future to make accurate predictions.

This article discusses the significance of generating synthetic data for developing robust machine learning models, especially in scenarios where real-world data is insufficient or flawed. It explores various statistical methods, including univariate and multivariate approaches such as Kernel Density Estimation and Bayesian Networks, emphasizing their practical applications and benefits in ensuring data privacy while accurately capturing relationships between features. Through a use case involving customer service call durations, the article illustrates the step-by-step process of implementing these techniques to create reliable synthetic datasets that reflect the true distributions of original data.

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Published via Towards AI