{"blocks": [{"key": "0c990135", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "f84b5d6c", "text": "Rapid-fire white-board review of core supervised-learning concepts for a customer-facing ranking service.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "872f41ca", "text": "Question", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "322db3c7", "text": "Explain the bias–variance trade-off and how you diagnose it. List the key assumptions behind ordinary least-squares linear regression. How do you detect and mitigate over-fitting? Compare boosting and bagging ensembles. When would you choose each? Which metrics would you choose for an imbalanced binary classifier and why? How would you handle missing data during training? Why is the normal distribution so common in statistical modeling?", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "47735907", "text": "Hints", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}, {"key": "add20796", "text": "Keep answers concise; reference equations and business impact where relevant.", "type": "unstyled", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {}}], "entityMap": {}}