问题描述
我在glmnet(实现套索回归)中使用了k倍交叉验证，但是我无法由此生成ROC图表.
I was using k-fold cross validation in glmnet (which implements lasso regression), but I can’t make the ROC charts from this.
library(glmnet)
glm_net <- cv.glmnet(dev_x_matrix,dev_y_vector,family="binomial",type.measure="class")
phat <- predict(glm_net,newx=val_x_matrix,s="lambda.min")
那给我一个向量，看起来像拟合值的对数.在此之后，我试图生成一些ROC图表，但是它不起作用.我认为是由于glmnet中x和y对象的性质.你有什么想法.
That gets me a vector with what looks like a log of the fitted values. I was trying to generate some ROC charts after this but it did not work. I think it is because of the nature of the x and y objects which goes into the glmnet. Do you have any ideas.
推荐答案
require("glmnet")
只需更改度量，您将获得AUC.它不是ROC曲线，但提供了等效的信息.
Just change the measure and you will get AUC. It's not a ROC curve but provides equivalent information.
glm_net <- cv.glmnet(x, y, family="binomial", type.measure="auc")
plot(glm_net)
这里是我正在训练的模型中的一个示例，只是为了展示它的外观.顺便提一句.该算法非常快！
Here is an example in a model i'm training, just to show how it looks.BTW. The algorithm is extremely fast!
有关更多模型可视化技术的信息，请查看 ROCr程序包
For more model visualization techniques, check out the ROCr package
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