Topic 14

Question 1

p(x) in Logistic Regression for Binary Classification

Answer 1

p(x) = e^β0+ β1x / 1 + e^β0+ β1x

Question 2

Logisitic Equation for Binary Classification

Answer 2

log p(x) / 1 - p(x) = β0+ β1x (+ … + βnxn for multiple predictor features)

Question 3

Decision Boundary (in Logisitic Regression)

Answer 3

A threshold that separates different classes based on the predicted probability