题目2574 · 机器学习
Why Bagging Helps Unstable Learners Most 10
Why does bagging usually help deep trees much more than it helps already-stable learners?
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中文题目题目2575 · 机器学习
Why Bagging Rarely Fixes High Bias 11
Why should you not expect bagging alone to rescue a learner whose individual trees are systematically misspecified?
打开 →题目2413 · 机器学习
Why Bagging Mainly Targets Variance
Why is bagging usually described as a variance-reduction tool rather than a bias-reduction tool?
打开 →题目2589 · 机器学习
Bagged MSE When Bias Stays Fixed 7
Assume each tree has the same squared bias b^2 and prediction noise floor nu, while bagging only changes the variance term according to the equicorrelated-tree formula. Derive the bagged test MSE with B trees.
打开 →题目2592 · 机器学习
Effective Independent Tree Count 8
Define B_eff by matching the correlated-forest variance sigma^2 [rho + (1-rho)/B] to the variance sigma^2 / B_eff of averaging independent trees. Derive B_eff.
打开 →题目2579 · 机器学习
Infer Tree Correlation From the Variance Floor 23
A single tree has variance 6, while an extremely large forest appears to level off at variance 1.8. What pairwise tree correlation rho is implied?
打开 →题目2573 · 机器学习
Infinite-Forest Variance Floor 2
Using the equicorrelated-tree variance formula, derive the prediction variance as the number of trees B tends to infinity.
打开 →题目2584 · 机器学习
Marginal Variance Reduction From One More Tree 3
Under the equicorrelated-tree model, derive how much the ensemble variance falls when you move from B trees to B+1 trees.
打开 →题目2572 · 机器学习
Numeric Ensemble Variance 22
Each tree has variance 9, pairwise correlation 0.2, and the forest has 25 trees. What is the variance of the forest average?
打开 →题目2585 · 机器学习
Trees Needed for a Target Variance Cap 4
Suppose each tree has variance sigma^2 and pairwise correlation rho. Derive the minimum B needed to make the ensemble variance at most V, assuming V > rho sigma^2.
打开 →题目2571 · 机器学习
Variance of an Average of Correlated Trees 1
Suppose B trees each have variance sigma^2 and every pair has correlation rho. Derive the variance of their simple average.
打开 →题目2593 · 机器学习
Why Averaging Cannot Cure Systematic Label Noise 20
Why can a larger forest fail to repair performance when the training labels themselves are systematically corrupted?
打开 →题目2576 · 机器学习
Why Feature Subsampling Helps When One Predictor Dominates 12
Why can random feature subsampling improve a forest when one very strong predictor would otherwise appear at the top of almost every tree?
打开 →题目2580 · 机器学习
Why More Trees Usually Do Not Create Classical Overfit 15
Why does adding more trees to a random forest typically plateau rather than create the kind of explosive overfit seen in some single-model families?
打开 →题目2591 · 机器学习
Why OOB Can Be Noisy on Small Samples 19
Why can out-of-bag error fluctuate a lot on a small dataset even when the forest itself is reasonably stable?
打开 →题目2577 · 机器学习
Why OOB Is Unsafe for Grouped or Temporal Data 13
Why can out-of-bag error be misleading when rows are linked by entity or time rather than being exchangeable?
打开 →题目2581 · 机器学习
Why Random-Forest Regression Extrapolates Poorly 16
Why does random-forest regression usually fail to extrapolate a trend far beyond the training range?
打开 →题目2578 · 机器学习
Why Tiny max_features Can Raise Bias 14
Why can making max_features too small hurt a random forest even though it lowers correlation?
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