A4 Logistic Regression Models - Formulate and solve a model to run logistic regressions - Use i=1,...,N observations (e.g. N=100 or 1000) - Use m=1,...,M randomized features x[i,m] (e.g., M=5) - Use realized dependent variables y[i] (0,1 binary) - Compute the optimal beta-coefficients (e.g., using the minos solver) - Compute the AIC0 of the "Null-model" with one feature only (i.e., a constant x_1=1) - Compute the AIC of the model with all M features - Compute the McFadden Pseudo R^2 := 1 - AIC/AIC0 Hint: Extend the linear regression model (linear_regression.txt) An objective is not needed. Solve the M-dimensional nonlinear system of equations (see regression lecture, slide 27)