Basics

• Deals with classification problems
• Very similar to linear regression

Pros and Cons

Pros

• Simple algorithm that is easy to implement, does not require high computation power.
• Performs extremely well when the data/response variable is linearly separable.
• Less prone to over-fitting, with low-dimensional data.
• Very easy to interpret, can give a measure of how relevant a predictor is and the association (positive or negative impact on response variable).

Cons

• Logistic regression has a linear decision surface that separates its classes in its predictions, in the real world it is extremely rare that you will have linearly separable data.
• Need to perform careful data exploration, logistic regression suffers with datasets with high multicollinearity between their variables, repetition of information can lead to wrong training of parameters.
• Requires that independent variables are linearly related to the log odds (log(p/(1-p)).
• Algorithm is sensitive to outliers.
• Hard to capture complex relationships, deep learning and classifiers such as Random Forest can outperform with more realistic datasets.

Additional Links/Resources

Advantages and Disadvantages of Logistic Regression