(2 pts) Recall that the Softmax function is a generalization of the logistic sigmoid for multiclass classification. In practice, machine learning software such as PyTorch uses a Softmax implementation for both binary and multiclass classification. Also recall that the Softmax function produces a vector output zRY and the logistic function a single scalar value z, representing class probabilities. Write the equation for a decision rule to produce y^ from the Softmax function in the binary case (when Y={0,1}; you can break ties arbitrarily). Write the decision rule to produce y^ from the logistic function. Compare the two rules. How are they similar and/or different? (1-2 sentences)