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For a binary classication problem, suppose a student proposes an algorithm \"A\" from some training data, and then test its performance to a testing data set, where 219 of these testing observations are from class 0 and the remaining 325 testing observations are from class 1. Using the proposed algorithm A, the student misclassies 32 observations from class 0 and misclassies 49 samples from class 1. 0.854 is the specicity of the algorithm A on this testing data For the previous binary classication problem , suppose another student proposes an algorithm \"B\" from another training data, and then test its performance to another testing data set, where 300 of these testing observations are from class 0 and the remaining 345 testing observations are from class 1. Using the proposed algorithm B, this student misclassies 24 observations from class 0 and misclassies 73 samples from class 1. What is the sensitivity of the algorithm B on this testing data