PAC Learning

Probably Approximately Correct (PAC) learning is a framework in machine learning that describes how algorithms can learn to make accurate predictions. It states that, with enough data and a good strategy, a learning algorithm can find a model that is close to the best possible one (approximately correct), and do so with high confidence (probably). Essentially, PAC learning provides guarantees that, under certain conditions, learning systems can reliably identify useful patterns without needing perfect accuracy, balancing the trade-off between confidence, accuracy, and the amount of data required.