一、Out of bag estimate(OOB) 1、OOB sample number RF是bagging的一种,在做有放回的bootstrap时,由抽样随机性可得到(其中1/e可由高数中的洛必达法则得到): RF中每次抽样N个样本训练每一棵decision tree(gt),对于此棵树gt,原始的数据集中将有近1/e(33.3%)的样本未参与其训练;因此可以使用这部分数据对此...
The bagging process may be tailored to a model’s specifications. The bootstrap training sample size should be near to that of the original set to achieve an accurate model. The number of iterations (trees) of the model (forest) should also be considered when determining the genuine OOB faul...
The OOB error is a prediction error estimation method used in machine learning models that involve bagging. It uses data samples not included in the bootstrap sample for creating the model, referred to as out-of-bag samples. How does the OOB error benefit machine learning models? The OOB ...
In order to develop the method, first, the 10 most significant input features are selected by using feature importance criteria through out-of-bag (OOB)... Islam,T,Rico-Ramirez,... - 《International Journal of Remote Sensing》 被引量: 23发表: 2014年 加载更多来源...
Many similar techniques exist for the same purpose, such as partial derivatives, odd ratios (Green et al., 2009), out-of-bag (OOB) predictor importance (Gupta et al., 2022), and recursive feature elimination (RFE) (Yan & Zhang, 2015). The same techniques can also be used to find ...