2 months ago
- LambdaMART is a flexible ranking algorithm that allows optimization of different relevance metrics like DCG.
- It uses pairwise swapping to compute DCG impact (deltas) and learns from model errors via gradient boosting.
- The implementation in Python with Pandas involves computing deltas via self-joins, weighing by rho, and training decision trees iteratively.
- Key steps include feature preparation, delta calculation, error weighting with rho, lambda accumulation, and ensemble training.
- Learning rate and model size are important considerations to prevent overfitting and manage performance.