我有如下两个模型:
library(mlbench)data(Sonar)library(caret)set.seed(998)my_data <- SonarfitControl <- trainControl( method = "boot632", number = 10, classProbs = T, savePredictions = "final", summaryFunction = twoClassSummary )modelxgb <- train( Class ~ ., data = my_data, method = "xgbTree", trControl = fitControl, metric = "ROC")library(mlbench)data(Sonar)library(caret)set.seed(998)my_data <- SonarfitControl <- trainControl( method = "boot632", number = 10, classProbs = T, savePredictions = "final", summaryFunction = twoClassSummary )modelsvm <- train( Class ~ ., data = my_data, method = "svmLinear2", trControl = fitControl, metric = "ROC")我想在同一个ggplot中绘制这两个模型的ROC曲线。
我正在做以下操作来生成曲线的点:
for_lift_xgb = data.frame(Class = modelxgb$pred$obs, xgbTree = modelxgb$pred$R)for_lift_svm = data.frame(Class = modelsvm$pred$obs, svmLinear2 = modelsvm$pred$R)lift_obj_xgb = lift(Class ~ xgbTree, data = for_lift_xgb, class = "R")lift_obj_svm = lift(Class ~ svmLinear2, data = for_lift_svm, class = "R")将这两个曲线绘制在同一张图上并使用不同颜色表示的最简单方法是什么？我还希望在图上标注各个模型的AUC值。
回答：
在构建模型后，您可以将预测结果合并到一个数据框中:
for_lift = data.frame(Class = modelxgb$pred$obs, xgbTree = modelxgb$pred$R, svmLinear2 = modelsvm$pred$R)使用以下代码构建lift对象:
lift = lift(Class ~ xgbTree + svmLinear2, data = for_lift, class = "R")并使用ggplot绘图:
library(ggplot)ggplot(lift$data)+ geom_line(aes(1-Sp , Sn, color = liftModelVar))+ scale_color_discrete(guide = guide_legend(title = "method"))
通过这种方式，您可以合并和比较多个模型。
要在图上添加AUC值，您可以创建一个包含模型名称、相应的AUC值和绘图坐标的数据框:
auc_ano <- data.frame(model = c("xgbTree","svmLinear2"), auc = c(pROC::roc(response = for_lift$Class, predictor = for_lift$xgbTree, levels=c("M", "R"))$auc, pROC::roc(response = for_lift$Class, predictor = for_lift$svmLinear2, levels=c("M", "R"))$auc), y = c(0.95, 0.9))auc_ano#output model auc y1 xgbTree 0.9000756 0.952 svmLinear2 0.5041086 0.90并将其传递给geom_text:
ggplot(lift$data)+ geom_line(aes(1-Sp , Sn, color = liftModelVar))+ scale_color_discrete(guide = guide_legend(title = "method"))+ geom_text(data = auc_ano, aes(label = round(auc, 4), color = model, y = y), x = 0.1)
