Advantages:
- High accuracy
- Tends to work well for imbalanced classification problems
- Little time required for feature pre-processing
- Does not require distributional assumption (only need to specify loss function)
- Handles both numeric and categorical data types
- Can extract feature importances and use base concept of decision tree for interpretation
Disadvantages:
- Requires some computing power and time spent in parameter tuning
- No direct interpretation such as regression equation is produced in output