Consider a binary classification problem and the hypothesis function h(w,x) = WTX = Wo + w X1 + w2X2 + W3x} +w4x with w' =. Here X, x2 represent the dataset features. a). Derive an equation for the decision boundary represented by this model, considering the sigmoid function and logistic regression. (3 Marks) b). Suggest a feature transformation that leads to linear decision boundary. (2 Marks) Consider a binary classification problem and the hypothesis function h(w,x) = WTX = Wo + w X1 + w2X2 + W3x} +w4x with w' =. Here X, x2 represent the dataset features. a). Derive an equation for the decision boundary represented by this model, considering the sigmoid function and logistic regression. (3 Marks) b). Suggest a feature transformation that leads to linear decision boundary. (2 Marks)