Random Forests.¶
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5-32.
Cited by¶
3 citations across 3 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Population Coding
- The partially-independent noise is the crux the prime identifies: bagging and random feature selection are deliberately engineered to decorrelate the trees' errors, because pooling improves accuracy precisely when per-element errors are partially independent — if all trees made the same mistakes, averaging would help nothing.
This sourceShows that generalization error depends on the strength of individual trees and the correlation between them, so bagging and random feature selection decorrelate errors, the condition under which ensemble averaging improves accuracy.
Supported in partVerified against the publisher's abstract
Breiman's abstract ties a forest's generalization error to tree strength and the correlation between trees, backing the frame but not the 'engineered to decorrelate' mechanism.
“The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them.”
- The partially-independent noise is the crux the prime identifies: bagging and random feature selection are deliberately engineered to decorrelate the trees' errors, because pooling improves accuracy precisely when per-element errors are partially independent — if all trees made the same mistakes, averaging would help nothing.
- Wisdom of the Crowds
- The Random Forest deliberately injects decorrelation by training each tree on bootstrapped data and random feature subsets, an engineering choice Breiman (2001) introduced precisely because the variance reduction from averaging scales with how uncorrelated the trees are.
This sourceIntroduces the Random Forest, deliberately decorrelating trees via bootstrap aggregation and random feature subsets so that averaging reduces variance; explicitly identifies inter-tree correlation as the bound on ensemble error.
- The Random Forest deliberately injects decorrelation by training each tree on bootstrapped data and random feature subsets, an engineering choice Breiman (2001) introduced precisely because the variance reduction from averaging scales with how uncorrelated the trees are.
Mechanisms¶
- Ensemble Model
- Its strength is squarely the bias–variance bargain: averaging decorrelated learners drives down variance
This sourceShows that averaging randomized tree predictors reduces ensemble error as correlation among their residuals falls.
- Its strength is squarely the bias–variance bargain: averaging decorrelated learners drives down variance
Verification¶
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