(а) Explаin why lineаr regressiоn is nоt generally an apprоpriate model for binary classification. (b) Write the two main computation steps of logistic regression, beginning with the linear score z. (c) For z = 1, use sigmoid(1) approximately equal to 0.731. With a classification threshold of 0.5, state the predicted class. (d) If the true label is y = 1, write the binary cross-entropy loss for this example and compute its approximate value using -ln(0.731) approximately equal to 0.313.