Lab Python ML – Challenge 6: Learning-to-Rank with Tree Ensembles for NLP Part 1 скачать в хорошем качестве

Lab Python ML – Challenge 6: Learning-to-Rank with Tree Ensembles for NLP Part 1

This video covers Learning-to-Rank (LTR) for question–answer retrieval using tree-based models: • Feature engineering from text pairs (lexical overlap + semantic embeddings) • Baselines (e.g., Random Forest) and common failure mode: high recall but many false positives • LambdaMART / gradient-boosted trees optimized for ranking metrics • Proper query-level train/val/test splits to avoid leakage • Evaluation with MRR, MAP, NDCG, plus calibration/diagnostics and feature importance • Optional exploration: lightweight personalization signals from user history #learningtorank #nlp #informationretrieval #lambdamart #xgboost #ranking #machinelearning