Understand it before memorizing it.
Sequential trees correct remaining gradient errors while regularization controls complexity.
Where this model earns its place
Tabular ranking/classification/regression
Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.
Competitions
Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.
Production baselines
Start with a simpler baseline then compare this model using the same evaluation split and operational constraints.
Trade-offs matter more than popularity.
Strengths
✓ Accurate
✓ Handles missing values
✓ Regularized
Limitations
△ Many hyperparameters
△ Can overfit
Metrics to watch
Before it reaches real users
- 01
Early stopping
Document the assumption and instrument the condition so regressions can be detected.
- 02
Feature leakage checks
Document the assumption and instrument the condition so regressions can be detected.
- 03
Calibrate probabilities
Document the assumption and instrument the condition so regressions can be detected.