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I am implementing logistic regression with stochastic gradient descent, but it is not working as expected. I've tried many epochs and different learning rates $\alpha$ but the probability of belonging to the correct class oscillates around $0.5$. I have got two classes denoted as $0,1$ and two features. Is this scheme correct?:

Initialize weights $w = [w_0, w_1, w_2]$ to some random number (I choose 1) and loop:

  1. Choose random training sample $x_i = [1, x_1, x_2]$.
  2. Calculate the probability of it belonging to class $1,$ $$P(\hat{y} = 1) = \frac{1}{1+e^{-w \cdot x_i}}$$
  3. Update the weights according to $$w = w + \alpha x_i(y_i - P(\hat{y} = 1))$$

This scheme makes sense to me since the update to the weights would be big if the probability of belonging to class $1$ was low, if the correct class was $1$ but I can't verify it more than that.

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  • $\begingroup$ Instead of showing the formula you intended to implement, please show the implementation itself (a minimal code example that people can run). $\endgroup$ – Mathias Müller Feb 16 at 11:55
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Hard to say without more detail, but isn't your update wrong? you need to subtract rather than add the gradient. Unless alpha is negative, this is wrong.

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