PAC (Probably Approximately Correct) Learning

PAC (Probably Approximately Correct) Learning is a framework in machine learning that describes how algorithms can learn from data with a guarantee of success. It states that, with enough examples, an algorithm can find a hypothesis that's close to the best possible in terms of accuracy, with high probability. Essentially, it ensures that a model will perform well on new data, not just the training data, by balancing the amount of data needed and the confidence level. PAC helps understand the theoretical limits and effectiveness of learning algorithms in making accurate predictions.