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Logistic regression transforms geometric relationships into probability predictions through a step-by-step process. Starting with linear transformation (ax + b) to create logits, the model applies exponential functions and sigmoid activation to map any real number to a probability between 0 and 1. The geometric aspect becomes clear in higher dimensions where the decision boundary forms lines or planes, with logits representing signed distance from these boundaries. This fundamental approach demonstrates how machine learning models convert spatial relationships into probabilistic predictions.

The Anatomy of Logistic Regression