👉 Samples computing is a method used in machine learning to evaluate and compare the performance of different models or algorithms by generating synthetic data that mimics real-world scenarios. This process involves creating a large dataset of labeled examples, often using techniques like data augmentation or generative models, to train and test various machine learning systems. By comparing the outputs of these models on the same synthetic samples, researchers and practitioners can assess their accuracy, robustness, and generalization capabilities without relying on potentially scarce or sensitive real-world data. This approach is particularly valuable in scenarios where obtaining labeled data is challenging, expensive, or time-consuming, allowing for efficient model selection and optimization.
