{"blocks": [{"key": "6a1e7604", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "c4de73cd", "text": "You are building a model to predict marketing outcomes and need to choose algorithms and features.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "69aac2c8", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "8d00cb3a", "text": "How would you use XGBoost (or gradient-boosted trees) for this task—outline training, tuning, and evaluation steps. Describe your approach to feature selection when the candidate feature set is large.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "79e0a336", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "550a6841", "text": "Mention cross-validation, regularization, SHAP/feature importance, domain knowledge, and avoiding leakage.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}