Compute out-of-bag cross-validated feature contributions to visualize model structures of randomForest models.Soren Havelund Welling
【Key words】Bagging; Out-of-bag sample; Cross-validation method; Generalization error; Double-bagging; Random forest 0引言 集成学习是一种新的学习范式,它使用多个学习机来解决同一个问题。由于它能显著提高一个学习系统的泛化能力,从20世纪90年代开始,对集成学习理论和算法的研究一直是机器学习领域中的热点...
The Out-of-Bag (OOB) error is a method of measuring the prediction error of random forests, bagged decision trees, and other machine learning models utilizing bootstrap aggregating (bagging). It provides an accurate estimate of the model performance without the need for cross-validation, making ...
We can eliminate most of the bias in the out-of-bag estimate and increase accuracy by incorporating a correction based on the distribution of the out-of-bag votes. 展开 关键词: bagging cross-validation generalization error DOI: 10.1023/A:1013964023376 被引量: 164 ...
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The Out-of-Bag error for a given training data point is estimated by computing the average difference between the actual value for a given training data point and the predictions based on trees that do not include the training data point in their bootstrap sample. Each tree is built on a ...
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