Math — Logistic Regression
Modelling a binary outcome.
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p = \frac{1}{1 + e^{-(\beta_0 + \beta_1 x)}}
\ln\frac{p}{1 - p} = \beta_0 + \beta_1 x
\text{fit by maximum likelihood}
\text{decision boundary at } p = 0.5
Modelling a binary outcome.
p = \frac{1}{1 + e^{-(\beta_0 + \beta_1 x)}}
\ln\frac{p}{1 - p} = \beta_0 + \beta_1 x
\text{fit by maximum likelihood}
\text{decision boundary at } p = 0.5