Say that you have the following initial settings for binary logistic regression: x = [1, 1, 3] w = [0, -2, 0.75] b = 0.5 2. Given that x's label is 1, what is the value of w_1, w_2, and w_3 at time t + 1 if the learning rate is 1? For this problem, you may ignore the issue of updating the bias term. 3. What is the value of P(y = 1 | x) given your updated weights
Say that you have the following initial settings for binary logistic regression:
x = [1, 1, 3]
w = [0, -2, 0.75]
b = 0.5
2. Given that x's label is 1, what is the value of w_1, w_2, and w_3 at time t + 1 if the learning rate is 1?
For this problem, you may ignore the issue of updating the bias term.
3. What is the value of P(y = 1 | x) given your updated weights from the previous question?
4. Given that x's label is 1, what is the value of the bias term at time t + 1 if the learning rate is 1?
5. What is the value of P(y = 1 | x) given both your updated weights and your updated bias term?
6. Given that x's label is 0, what is the value of P(y = 0| x) at time t + 1 if the learning rate is 0.1?
Round your answer to the nearest 1000th as a number [0, 1].
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