0 0 votes Suppose that you have trained a logistic regression classifier $h_{\theta}(x) = \sigma(1 - x)$ where $\sigma(.)$ is the logistic/sigmoid function. What does its output on a new example $x = 2$ mean? Check all that apply.(Hint: $\sigma(-1) = 0.27$)Your estimate for $P(y = 1|x; \theta)$ is about 0.73.Your estimate for $P(y = 0|x; \theta)$ is about 0.27.Your estimate for $P(y = 1|x; \theta)$ is about 0.27.Your estimate for $P(y = 0|x; \theta)$ is about 0.73. Machine Learning goclasses goclasses-da-course machine-learning logistic-regression multiple-selects + – GO Classes 164 views answer comment Share Follow Print 0 reply Please log in or register to add a comment.
0 0 votes $P(y = 1|x)$ is estimated by $h_o(x)$. So when $x = 2, P(y = 1|x = 2)$ is estimated by $h_{\theta}(2) = \sigma (-1) \approx 0.27$, also meaning that $P(y = 0 | x = 2) \approx 1 - 0.27 = 0.73$. GO Classes answered Mar 18, 2025 GO Classes comment Share Follow 0 reply Please log in or register to add a comment.
