What is the PAC framework?

title: 'Fig. 1: Comparison of the strengths of humans and statistical ML machines, illustrating the complementary ways they generalise in human-AI teaming scenarios. Humans excel at compositionality, common sense, abstraction from a few examples, and robustness. Statistical ML excels at large-scale data and inference efficiency, inference correctness, handling data complexity, and the universality of approximation. Overgeneralisation biases remain challenging for both humans and machines. Collaborative and explainable mechanisms are key to achieving alignment in human-AI teaming. See Table 3 for a complete overview of the properties of machine methods, including instance-based and analytical machines.'

The PAC (Probably Approximately Correct) framework is a theoretical framework that analyzes whether a model (i.e., a product) derived via a machine learning algorithm (i.e., a generalization process) from a random sample of data can be expected to achieve a low prediction error on new data from the same distribution in most cases[1]. This framework is foundational in understanding model generalization in statistical AI and is particularly relevant in evaluating how well machine learning models can infer patterns and make accurate predictions on unseen data.


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