英文:
How to make beautiful ROC curves for two models in the same plot?
问题
I've trained two xgboost models, say model1 and model2. I have the AUC scores for each model and I want them to appear in the plot. I want to make beautiful ROC curves for both models in the same plot. Something like this:
How can I do that?
I usually use the library pROC, and I know I need to extract the scores, and the truth from each model, right?
so something like this maybe:
roc1 = roc(model1$truth, model1$scores)roc2 = roc(model2$truth, model2$scores)
I also need the fpr and tpr for each model:
D1 = data.frame(fpr = 1 - roc1$specificities, tpr = roc1$sensitivities)D2 = data.frame(fpr = 1 - roc2$specificities, tpr = roc2$sensitivities)
Then I can maybe add arrows to point out which curve is which:
arrows = tibble(x1 = c(0.5, 0.13) , x2 = c(0.32, 0.2), y1 = c(0.52, 0.83), y2 = c(0.7, 0.7))
And finally ggplot: (this part is missing)
ggplot(data = D1, aes(x = fpr, y = tpr)) +geom_smooth(se = FALSE) +geom_smooth(data = D2, color = 'red', se = FALSE) +annotate("text", x = 0.5, 0.475, label = "score of model 1") +annotate("text", x = 0.13, y = 0.9, label = "scores of model 2")
So I need help with two things:
-
How do I get the right information out from the models, to make ROC curves? How do I get the
truthand theprediction scores? Thetruthare just the labels of the target feature in the training set maybe? -
How do I continue the code? and is my code right so far?
英文:
I've trained two xgboost models, say model1 and model2. I have the AUC scores for each model and I want them to appear in the plot. I want to make beautiful ROC curves for both models in the same plot. Something like this:
How can I do that?
I usually use the library pROC, and I know I need to extract the scores, and the truth from each model, right?
so something like this maybe:
roc1 = roc(model1$truth, model1$scores)roc2 = roc(model2$truth, model2$scores)
I also need the fpr and tpr for each model:
D1 = data.frame = (fpr = 1 - roc1$specificities, tpr = roc1$sensitivities)D2 = data.frame = (fpr = 1 - roc2$specificities, tpr = roc2$sensitivities)
Then I can maybe add arrows to point out which curve is which:
arrows = tibble(x1 = c(0.5, 0.13) , x2 = c(0.32, 0.2), y1 = c(0.52, 0.83), y2 = c(0.7,0.7) )
And finally ggplot: (this part is missing)
ggplot(data = D1, aes(x = fpr, y = tpr)) +geom_smooth(se = FALSE) +geom_smooth(data = D2, color = 'red', se = FALSE) +annotate("text", x = 0.5, 0.475, label = 'score of model 1') +annotate("text", x = 0.13, y = 0.9, label = scores of model 2') +
So I need help with two things:
-
How do I get the right information out from the models, to make ROC curves? How do I get the
truthand theprediction scores? Thetruthare just the labels of the target feature in the training set maybe? -
How do I continue the code? and is my code right so far?
答案1
得分: 3
以下是已翻译的内容：
You can get the sensitivity and specificity in a data frame using coords from pROC. Just rbind the results for the two models after first attaching a column labeling each set as model 1 or model 2. To get the smooth-looking ROC with automatic labels you can use geom_textsmooth from the geomtextpath package:
library(pROC)library(geomtextpath)roc1 <- roc(model1$truth, model1$scores)roc2 <- roc(model2$truth, model2$scores)df <- rbind(cbind(model = "Model 1", coords(roc1)),cbind(model = "Model 2", coords(roc2)))ggplot(df, aes(1 - specificity, sensitivity, color = model)) +geom_textsmooth(aes(label = model), size = 7, se = FALSE, span = 0.2,textcolour = "black", vjust = 1.5, linewidth = 1,text_smoothing = 50) +geom_abline() +scale_color_brewer(palette = "Set1", guide = "none", direction = -1) +scale_x_continuous("False Positive Rate", labels = scales::percent) +scale_y_continuous("True Positive Rate", labels = scales::percent) +coord_equal(expand = FALSE) +theme_classic(base_size = 20) +theme(plot.margin = margin(10, 30, 10, 10))
Data used
set.seed(2023)model1 <- model2 <- data.frame(scores = rep(1:100, 50))p1 <- model2$scores + rnorm(5000, 0, 20)p2 <- model1$scores/100model1$truth <- rbinom(5000, 1, (p1 - min(p1))/diff(range(p1)))model2$truth <- rbinom(5000, 1, p2)
英文:
You can get the sensitivity and specifity in a data frame using coords from pROC. Just rbind the results for the two models after first attaching a column labelling each set as model 1 or model 2. To get the smooth-looking ROC with automatic labels you can use geom_textsmooth from the geomtextpath package:
library(pROC)library(geomtextpath)roc1 <- roc(model1$truth, model1$scores)roc2 <- roc(model2$truth, model2$scores)df <- rbind(cbind(model = "Model 1", coords(roc1)),cbind(model = "Model 2", coords(roc2)))ggplot(df, aes(1 - specificity, sensitivity, color = model)) +geom_textsmooth(aes(label = model), size = 7, se = FALSE, span = 0.2,textcolour = "black", vjust = 1.5, linewidth = 1,text_smoothing = 50) +geom_abline() +scale_color_brewer(palette = "Set1", guide = "none", direction = -1) +scale_x_continuous("False Positive Rate", labels = scales::percent) +scale_y_continuous("True Positive Rate", labels = scales::percent) +coord_equal(expand = FALSE) +theme_classic(base_size = 20) +theme(plot.margin = margin(10, 30, 10, 10))
Data used
set.seed(2023)model1 <- model2 <- data.frame(scores = rep(1:100, 50))p1 <- model2$scores + rnorm(5000, 0, 20)p2 <- model1$scores/100model1$truth <- rbinom(5000, 1, (p1 - min(p1))/diff(range(p1)))model2$truth <- rbinom(5000, 1, p2)
通过集体智慧和协作来改善编程学习和解决问题的方式。致力于成为全球开发者共同参与的知识库，让每个人都能够通过互相帮助和分享经验来进步。
评论