Inference, estimation, and decision-making from data
Linear regression predicts any real number, which is awkward for a yes-or-no outcome. A probability has to stay between 0 and 1. Logistic regression fixes this by computing a linear score first, then squashing that score into the interval (0, 1) with the sigmoid function, written σ. The decision boundary underneath stays linear; only the output becomes a genuine probability.
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▶ Logistic Regression