Machine Learning
平衡随机森林
Balanced Random ForestA machine learning ensemble method designed to handle imbalanced datasets by randomly undersampling the majority class in each bootstrap sample. / 一种旨在通过在每个自助样本中随机欠采样多数类来处理不平衡数据集的机器学习集成方法。
- Knowledge type
- 数学理论
- First proposed or discovered
- Proposer
- Turkey Farm member
- Creator
- 农场主
- Last updated
- 2026-11-10 20:06:45
Sub-knowledge points
Bagging ensemble techniqueRandom feature selectionClass imbalance handling
Higher-order knowledge points
Random Forest / 数学理论Metaheuristic optimization
Main parameters and performance indicators
n_estimators: Number of trees in the forestmax_features: Number of features to consider when looking for the best splitclass_weight: 'balanced' to adjust weights inversely proportional to class frequencies
Open problems
No unresolved problem recorded yet
Related literature
ranked by community starsAddressing imbalanced medical data classification using integrated balanced random forest with na-ture-inspired metaheuristicsOpen source
Related products and links
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Creation and modification trail
1 recordsRecord title: Balanced Random Forest.
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