Author(s): Felix Pappe

Originally published on Towards AI.

Go inside the training loop and watch the model learn

If you’ve ever wondered what statistics packages and programs are doing when calculating logistic regression, this is for you.

The article walks through how logistic regression turns inputs into probabilities using the sigmoid function, framing the learning problem as maximizing likelihood (and minimizing the resulting cross-entropy/binary log-loss). It then derives the gradient needed for optimization, explains how gradient descent updates model parameters iteratively using a learning rate, and connects each math step to an example “online shop” dataset. Finally, it illustrates the first parameter update and how repeating updates over many iterations makes the learned sigmoid curve better match the data, including why input standardisation improves training stability and how to convert learned parameters back to the original feature scale for interpretation.

Read the full blog for free on Medium.

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Published via Towards AI