类似于h2o软件包中排列精度的重要性 [英] something similar to permutation accuracy importance in h2o package

问题描述

我用R中的randomForest程序包为我的多项目标设置了一个随机森林.寻找变量重要性后，我发现了permutation accuracy importance，这就是我要进行分析的目的. 我也为h2o程序包安装了一个随机森林，但是显示给我的唯一度量是relative_importance, scaled_importance, percentage.

I fitted a random forest for my multinomial target with the randomForest package in R. Looking for the variable importance I found out permutation accuracy importance which is what I was looking for my analysis. I fitted a random forest with the h2o package too, but the only measures it shows me are relative_importance, scaled_importance, percentage.

我的问题是:我可以提取一个指标来显示目标的水平，从而更好地对我想在考试中参加的变量进行分类吗? Permutation accuracy importance是我在这种情况下可以使用的最佳措施吗?

My question is: can I extract a measure that shows me the level of the target which better classify the variable i want to take in exam? Permutation accuracy importance is the best measure I can use in this case?

例如:我有一个3个级别的目标:ABC和5个变量:v1-v2-v3-v4-v5是否有一项措施向我表明v1对目标的A级比B级更重要(类似于排列精度的重要性)?

For example: I have a 3 levels target: A-B-C and 5 variables: v1-v2-v3-v4-v5 Is there a measure that shows me that v1 is more important for the level A of the target rather than level B (something similiar to the permutation accuracy importance)?

推荐答案

尽管h2o不通过r/python api提供permutation accuracy importance(正如您指出的，它提供了不同的重要性)，但是您可以使用PDP h2o.partialPlot()进行查看功能中的各个级别如何影响目标.

While h2o doesn't provide permutation accuracy importance (as you pointed out it provides variable importance) through the r/python api, you can use PDP h2o.partialPlot() to see how individual levels within a feature impact the target.

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