r语言中roc曲线二分类

在R语言中，可以使用plotly包来可视化二分类模型的ROC曲线。首先，你需要计算出模型的真正率（True Positive Rate）和假正率（False Positive Rate），然后使用plotly函数来画出ROC曲线。在曲线中添加AUC（Area Under the Curve）值可以使用annotate函数来实现。以下是一个示例代码：

# 加载plotly包
library(plotly)

# 计算真正率和假正率
true_positive_rate <- c(0.2, 0.4, 0.6, 0.8, 1)
false_positive_rate <- c(0, 0.2, 0.4, 0.6, 1)

# 绘制ROC曲线
roc_curve <- plot_ly(x = false_positive_rate, y = true_positive_rate, type = "scatter", mode = "lines")
roc_curve <- roc_curve %>% add_trace(x = c(0,1), y = c(0, 1), mode = "lines", line = list(dash = "dash"), name = "Random")

# 添加AUC值
roc_curve <- roc_curve %>% layout(annotations = list(x = 0.5, y = 0.5, text = paste("AUC =", auc, sep = ""), showarrow = FALSE))

# 显示ROC曲线
print(roc_curve)

这段代码会生成一个二分类模型的ROC曲线，并在曲线上方添加了AUC值。你可以将真正率和假正率替换为你的实际模型结果。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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