题目4216 · 机器学习
Normalized MDI Share 1
A 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?
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中文题目题目4218 · 机器学习
Normalized MDI Share 3
A 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 firs
打开 →题目4217 · 机器学习
Normalized MDI Share 2
A 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?
打开 →题目4219 · 机器学习
Normalized MDI Share 4
Two 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?
打开 →题目4220 · 机器学习
Normalized MDI Share 5
A 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?
打开 →题目2560 · 机器学习
Global Weight Rescaling Leaves Split Ranking Unchanged 5
If every sample weight in a node is multiplied by the same constant c>0, how does each candidate split's weighted impurity decrease change?
打开 →题目4224 · 机器学习
Grouped Permutation Drop Pattern 4
An 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?
打开 →题目4226 · 机器学习
High-Cardinality ID Trap
A 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?
打开 →题目4237 · 机器学习
Why Trees Overcredit Splittable Features
Why do impurity-based importances tend to overcredit features with many possible split points?
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