1.) When and Why do we use Proportional model, Binomial model, and Poisson regression model? 2.) How and why are degrees od freedom infleunced by each of these models?

This is a binary classification problem, y has two values (0 or 1), and X (feature) has three dimensions. • Use a logistic regression model to project X to y (classify X into two categories: 0 or 1). • The initialization is: w1 = 0, w2 = 0, w3 = 0, b = 0, Learning rate is 2. • You must use Gradient Descent for logistic regression in this question. • The regression should stop after one iteration.       Calculation process and formulas must be included in your answer! You must answer this question by manual calculation, but not programming.

give the steps by steps answer

Q.1 Explain the different regularization techniques used with Linear regression to handle multivariate data.

Q. What can surrogate models enable? Select all that apply.     A) They can help linearize strongly nonlinear forward models. B) They can help accelerate evaluation of the forward model or likelihood in Bayesian inference. C) They can make it easier to exploit parallel computing resources. D) They can reduce the dimensionality of the data. E) They can help push more computation to an "offline" phase of the analysis.