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Logistic regression (ML)

Logistic regression is a foundational probabilistic classifier that models the log-odds of a binary (or multinomial) outcome as a linear function of the predictors. Introduced by D. R. Cox in 1958, it remains one of the most widely used and interpretable classification methods in both statistics and machine learning, valued for its calibrated probability outputs and clear coefficient interpretation.

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Logistic Regression (Machine Learning Classification Model)

Taxonomisk metodpost · ml-model / machine-learning

Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. · DOI 10.1111/j.2517-6161.1958.tb00292.x
James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 4). Springer. · ISBN 978-1-4614-7138-7

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