5. Logistics Regression
Idea
- Applying the logistic function to compress the output of the linear function so that it's limited to a range between 0 and 1.
- The logistic function transforms real-valued input to an output number y between 0 and 1, interpreted as the probability the input object belongs to the positive class, given its input features.
Assumption
(1) Logistic regression requires observations to be independent of each other. ( In other words, the observations should not come from repeated measurements or matched data.)
(2) Independent variables should not be highly correlated with each other
(3) requires that the independent variables are linearly related to the log odds.
(4) typically requires a large sample size