Hands-On Artificial Intelligence for Cybersecurity

Logistic regression pros and cons

The advantages of adopting logistic regression can be summarized as follows:

• The model can be trained very efficiently
• It can be used effectively even in the presence of a large number of features
• The algorithm has a high degree of scalability, due to the simplicity of its scoring function

At the same time, however, logistic regression suffers from some important limitations, deriving from the basic assumptions that characterize it, such as the need for the features to be linearly independent (a rule that translates in technical terms as the absence of multicollinearity), as well as requiring more training samples on average than other competing algorithms, as the maximum likelihood criterion adopted in ...