[英]R random forest feature selection based on AUC
For binary option prediction (rise, fall) I am trying random forest in R but the importance measures and OOB are biased in my case 对于二元期权预测(上升,下降),我尝试使用R中的随机森林,但在我的情况下,重要性指标和OOB有偏差
I found this article but it is Python related. 我找到了这篇文章,但它与Python有关。
Is there an R package approach for automatic feature selection that 是否有用于自动特征选择的R包方法
I just came across this question, I found this package that might help you: 我刚遇到这个问题,我发现此软件包可能对您有帮助:
i. 一世。 It's called AUCRF, it performs feature selection in a random forest model based on optimizing AUC. 它称为AUCRF,它基于优化AUC在随机森林模型中执行特征选择。 https://cran.r-project.org/web/packages/AUCRF/AUCRF.pdf https://cran.r-project.org/web/packages/AUCRF/AUCRF.pdf
ii. II。 It does allow cross-validation of your AUC based selection AUCRFcv(x, nCV = 5, M = 20) 它确实允许对基于AUC的选择AUCRFcv(x,nCV = 5,M = 20)进行交叉验证
where nCV is number of folds, M = number of repeats. 其中nCV是折叠数,M =重复数。
iii. III。 Regarding allowing your own evaluation, it does have an option where you can specify the formula using ~ but you will have to explore that more for your specific case, since you have not provided test code. 关于允许您自己评估,它确实有一个选项,您可以在其中使用〜指定公式,但是由于您未提供测试代码,因此您将不得不针对特定情况进行更多研究。
Hope this helps! 希望这可以帮助!
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