Pros:
- Well-designed for multiclass problems
- Closed form solution, meaning computationally efficient
- Outputs class probabilities for each label
- Does not require much hyperparameter tuning
Cons:
- Assumes multivariate normality among features, though it is relatively robust to this assumption
- Not as well suited for mixed data types as decision tree based methods
