So I'm having a hard time conceptualizing how to make mathematical representation of my solution for a simple logistic regression problem. I understand what is happening conceptually and have implemented it, but I am answering a question which asks for a final solution.
Say I have a simple two column dataset denoting something like likelihood of getting a promotion per year worked, so the likelihood would increase the person accumulates experience. Where X denotes the year and Y is a binary indicator for receiving a promotion:
X | Y 1 0 2 1 3 0 4 1 5 1 6 1
I implement logistic regression to find the probability per year worked of receiving a promotion, and get an output set of probabilities that seem correct. I get an output weight vector that that is two items, which makes sense as there are only two inputs. The number of years X, and when I fix the intercept to handle bias, it adds a column of 1s. So one weight for years, one for bias.
So I have two few questions about this. Since it is easy to get an equation of the form y = mx + b as a decision boundary for something like linear regression or a PLA, how can similarly I denote a mathematical solution with the weights of the logistic regression model? Say I have a weights vector [0.9, -0.34], how can I convert this into an equation?
Secondly, I am performing gradient descent which returns a gradient, and I multiply that by my learning rate. Am I supposed to update the weights at every epoch? As my gradient never returns zeros in this case so I am always updating.
Thank you for your time.