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(a) Define Probably Approximately Correct (PAC) learning. What is its significance in machine learning? Given a learning algorithm with an error rate of 0.2 , how many samples should be drawn to achieve a confidence level of 95% in PAC learning? OR (b) Explain the K-Nearest Neighbors (KNN) algorithm. How does the choice of ' K ' 10 influence the model's behavior? Given a dataset with 120 samples, each having three features, implement the KNN algorithm with Manhattan distance as the similarity measure. If K=7, find the class label for a new data point with the following feature values: Featurel=6.3, Feature2=2.5, Feature 3=4.8