update

Author: mhjensen

doc/src/week47/Latexslides/answersweek47.tex

@@ -53,7 +53,7 @@ \section{Logistic Regression}
 3. True. It has an analytical closed-form solution for its parameters, analogous to the normal equation in linear regression, however it needs to be solved numerically.

 
 4. Its loss function (log loss) is convex, which guarantees a unique global optimum during training.
Logistic regression is a probabilistic classifier for binary outcomes, learned via maximum likelihood. Unlike linear regression, it does not have a closed-form coefficient solver and must be fit with iterative methods. Its negative log-likelihood (cross-entropy) cost is convex, ensuring a single global minimum .
-
+If you don't have a use the negative log-likelihood, then the function is concave and you have a maximization problem.
 \item[\textbf{6.}]\textbf{(True/False)}  
 Logistic regression produces a linear decision boundary in the input feature space.
 Answer: True. Logistic regression (with no feature transformations) produces a linear decision boundary in the input feature space.
True. The model is $\sigma(w^T x + b)$ and the decision boundary occurs at $w^T x + b = 0$, which is a hyperplane, that is a linear boundary.