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4216Normalized MDI Share 1A random forest reports total mean-decrease-in-impurity contributions spread=0.42, imbalance=0.21, id bucket=0.07. What are the normalized importance shares, and which feature ranks first?机器学习简单数值题未尝试面试订阅4217Normalized MDI Share 2A model has baseline validation AUC 0.62. After permuting three features separately, AUC becomes 0.57 for value signal, 0.60 for momentum, and 0.61 for zip code. What permutation-importance drops do these imply, and which feature ranks first?机器学习简单数值题未尝试面试订阅4218Normalized MDI Share 3A sector feature is represented by three one-hot columns with impurity-gain importances 0.04, 0.03, and 0.01. Two other features have importances 0.05 and 0.07. If you aggregate the one-hot block into a single group, what are the normalized group shares and which group ranks first?机器学习简单数值题未尝试面试订阅4219Normalized MDI Share 4Two trees contribute split gains to features A and B. Tree 1 contributes A=12, B=5. Tree 2 contributes A=8, B=10. What are the total normalized gain importances for A and B?机器学习简单数值题未尝试面试订阅4220Normalized MDI Share 5A model has baseline log loss 0.400. After permuting feature X, log loss rises to 0.455; after permuting feature Y, it rises to 0.420. What are the permutation importances under a log-loss metric, and which feature is more important?机器学习简单数值题未尝试面试订阅4221Grouped Permutation Drop Pattern 1A model starts at validation accuracy 0.82. Permuting feature X1 alone lowers it to 0.79, permuting X2 alone lowers it to 0.8, and permuting them together lowers it to 0.7. What are the three drops, and what pattern does that suggest?机器学习中等数值题未尝试面试订阅4223Grouped Permutation Drop Pattern 3A model has baseline AUC 0.70. With a correlated twin present, permuting feature A drops AUC to 0.64. After removing the twin, permuting A drops AUC to 0.58. By how much did feature A's permutation importance increase?机器学习中等数值题未尝试面试订阅4224Grouped Permutation Drop Pattern 4An impurity-based feature ranking is id hash=0.40, signal 1=0.35, signal 2=0.25. After limiting max depth, id hash gain is cut in half while the other raw gains are unchanged. What are the new normalized shares?机器学习中等数值题未尝试面试订阅4225Grouped Permutation Drop Pattern 5A feature's permutation importance is measured as baseline accuracy minus permuted accuracy. Under validation set A the numbers are 0.80 and 0.78; under noisier validation set B they are 0.74 and 0.72. What is the relative drop as a percentage of baseline in each case?机器学习中等数值题未尝试面试订阅4226High-Cardinality ID TrapA random forest says a hashed customer ID is the most important feature by impurity decrease, even though the validation permutation drop is almost zero. What is the most likely trap?机器学习中等derivation未尝试面试订阅4227Leakage Proxy TrapA model ranks a 'days since settlement' field as highly important in a fraud predictor, but that field is only known after the case outcome becomes visible. What is wrong with reading that as genuine predictive importance?机器学习中等derivation未尝试面试订阅4228Proxy Feature TrapA tree model gives most importance to ZIP code instead of the underlying income and region variables. Why should you be cautious before concluding ZIP code is the true driver?机器学习中等derivation未尝试面试订阅4229Correlation Split Credit TrapTwo nearly identical features alternate as top splitters across different random seeds. Does that mean the signal is unstable?机器学习中等derivation未尝试面试订阅4230Negative Permutation ImportanceA weak feature shows slightly negative permutation importance on a finite validation set. Should you immediately conclude it is genuinely anti-predictive?机器学习中等derivation未尝试面试订阅4231Grouped Permutation RemedyIf several sector dummy variables move together and share the same economic information, what diagnostic is often better than permuting one dummy at a time?机器学习中等derivation未尝试面试订阅4232Time-Split Remedy for Leakage RiskA feature may be available only with reporting delay. What evaluation setup is more convincing than random train/test splitting?机器学习中等derivation未尝试面试订阅4233Retrain-After-Drop CheckWhy can 'drop feature X and retrain' tell a different story from permutation importance on the original fitted model?机器学习中等derivation未尝试面试订阅4234Conditional Importance RemedyIf a feature is highly correlated with others, what is the point of conditional importance rather than plain marginal permutation?机器学习中等derivation未尝试面试订阅4235Stability RemedyIf importance rankings swing wildly across folds, what is the right reaction?机器学习中等derivation未尝试面试订阅4236Importance Is Not CausalityWhy is it dangerous to treat feature importance as if it were a causal ranking?机器学习中等essay未尝试面试订阅