# how to find an equation representing a decision boundary in logistic regression

I'm new to machine learning and currently working on logistic regression. but i don't know how to deal this problem. let us consider the logistic regression for a dataset $$(x_n,y_n)\ (x_i \in \mathbb R^d, y_i \in \{+1,-1\})$$, let $$\Phi(x)=(\phi_p(x))^T$$ be a p-dimensional vector of functions and $$x\in\mathbb R^d$$ be a parameter vector. A probabilistic model is defined as $$p(y|x)=\frac{1}{1+\exp(-y\theta^T\Phi(x))}$$ $$(p(+1|x)=\frac{1}{1+\exp(-\theta^T\Phi(x))}=\frac{\exp(\theta^T\Phi(x))}{1+\exp(\theta^T\Phi(x))},p(-1|x)=\frac{1}{1+\exp(\theta^T\Phi(x))})$$ and the logistic regression estimates the parameter $$\theta$$ by maximizing $$L(\theta)=\sum_{i=1}^n\log p(y_i|x_i,\theta)$$.

Then, how can I found an equation representing a decision boundary which satisfy $$p(+1|x)=p(-1|y)$$?

Then, how can I found an equation representing a decision boundary which satisfy $$p(+1|x)= p(−1|x)$$?
(correcting $$p(-1|y)$$ to $$p(-1|x)$$). The decision boundary is given by the set $$\left\{x\in\mathbb{R}^d:p(+1|x) = p(-1|x)\right\}.$$ Then expanding the condition, \begin{align*} \frac{1}{1+\exp(-\theta^T\Phi(x))} &= \frac{1}{1+\exp(\theta^T\Phi(x))} \\ 1+\exp(-\theta^T\Phi(x)) &= 1+\exp(\theta^T\Phi(x)) \\ -\theta^T\phi(x) &= \theta^T\Phi(x)\\ \Rightarrow \theta^T\Phi(x) &= 0. \end{align*} And so the decision boundary is given by the set, $$\{x\in\mathbb{R}^d:\theta^T\Phi(x) = 0\}.$$
• Thank you for the the answer. For $x\in\mathbb R^2$ and $\Phi (x)=(x_1,x_2)^T$, is the decision boundary $\theta_1x_1=-\theta_2x_2$? Jun 2 '20 at 0:43
• Yes, that would follow from $\theta^T\Phi(x) = 0$ . Jun 2 '20 at 6:48