1. Logistic Regression Assume in a binary classification problem, we need to predict a binary label ye 1-1, +1} for a feature vector x = [x0.x]. In logistic regression, we can reformulate the binary classification problem in a probabilistic framework: We aim to model the distribution of classes given the input feature vector x. Specifically, we can express the conditional probability p(x) parameterized by (w, b) using a logistic function. Assume the probability of the positive prediction ply = +11x) is represented as: ply = +1x) = 1 + e-( wxb) 1.1 (10 points) Basic Formulation 1.1.1 Please derive the formulation of ply= -11x). 1.1.2 Please show that p(y|x) = 1 + e-w's+)