logistic regression

Advantage

1. It is convenient for generating probability scores.
2. Efficient implementation is available across different tools.
3. The issue of multicollinearity can be countered with regularisation.
4. It has widespread industry use.

Cons

1. It does not perform well when the features space is too large.
2. It does not perform well when there are a lot of categorical variables in the data.
3. The nonlinear features have to be transformed to linear features in order to efficiently use them for a logistic model.
4. It relies on entire data i.e. if there is even a small change in the data, the logistic model can change significantly.