We train a logistic regression model on a dataset where each datapoint has a single feature x and a target variable y, where y {0,1}. The resulting expression for the prediction of y is as follows: (x) 1 1+ exp (-(2x 1)) where the coefficients are Bo = -1, = 2. A) Circle the correct probabilistic interpretation(s) of (x): (a) P(y = 1|x) (b) P(y = 0[x) (c) 1 - P(y = 11x) (d) 1 - P(y = 0[x) (e) None of the above B) Which of the following condition(s) will lead to a prediction of y = 1? (a) (x) > 0.5 (b) P(y=1[x) P(y=0\x) 21 (c) (2x 1) 20 (d) None of above C) Suppose we collect another datapoint for our training dataset with x = 0.5 and y = 1, and we want to maximize the log likelihood of our model. What are the updated values of Bo and 1 after one iteration of gradient descent that uses only this new datapoint? Assuming a step size a, your answer should be simplified to an expression of a. We train a logistic regression model on a dataset where each datapoint has a single feature x and a target variable y, where y {0,1}. The resulting expression for the prediction of y is as follows: (x) 1 1+ exp (-(2x 1)) where the coefficients are Bo = -1, = 2. A) Circle the correct probabilistic interpretation(s) of (x): (a) P(y = 1|x) (b) P(y = 0[x) (c) 1 - P(y = 11x) (d) 1 - P(y = 0[x) (e) None of the above B) Which of the following condition(s) will lead to a prediction of y = 1? (a) (x) > 0.5 (b) P(y=1[x) P(y=0\x) 21 (c) (2x 1) 20 (d) None of above C) Suppose we collect another datapoint for our training dataset with x = 0.5 and y = 1, and we want to maximize the log likelihood of our model. What are the updated values of Bo and 1 after one iteration of gradient descent that uses only this new datapoint? Assuming a step size a, your answer should be simplified to an expression of a