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Quantitative Finance · Glossary

What is Logistic regression?

Definition 4.6 Machine Learning for Markets · Chapter 4 — Linear and Regularised Baselines

Logistic regression models P(y=1∣x)=1/(1+e−(b+x⊤β))\P(y = 1\mid x) = 1/(1 + e^{-(b + x^\top\beta)}) and fits (b,β)(b,\beta) by maximum likelihood, usually with a ridge or lasso penalty on β\beta.

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