The following are all benefits of generalized additive models (GAMS), EXCEPT: GAMS are less computationally demanding than linear regression. GAMS can model non-linear relationships that standard linear regression will miss. GAMS can potentially make more accurate predictions of the response than linear regression can. One can examine the effect of each predictor on the response individually while holding all of the other predictors fixed.

The benefits of switching to all-subsets regression from stepwise regression are broken forth in great depth below.   .

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.

Assume the following simple regression model, Y = β0 + β1X + ϵ ϵ ∼ N(0, σ^2 )   Now run the following R-code to generate values of σ^2 = sig2, β1 = beta1 and β0 = beta0. Simulate the parameters using the following codes:   Code:   # Simulation ##  set.seed("12345")  beta0 <- rnorm(1, mean = 0, sd = 1) ## The true beta0  beta1 <- runif(n = 1, min = 1, max = 3) ## The true beta1  sig2 <- rchisq(n = 1, df = 25) ## The true value of the error variance sigmaˆ2    ## Multiple simulation will require loops ##  nsample <- 10 ## Sample size n.sim <- 100 ## The number of simulations  sigX <- 0.2 ## The variances of X #   # Simulate the predictor variable ##  X <- rnorm(nsample, mean = 0, sd = sqrt(sigX))   Q1  Fix the sample size nsample = 10 . Here, the values of X are fixed. You just need to generate ϵ and Y . Execute 100 simulations (i.e., n.sim = 100). For each simulation, estimate the regression coefficients (β0, β1) and the error variance (σ 2 ). Calculate the mean of…