我正在尝试为神经网络创建一个ROC图。我似乎无法让它发挥作用。我收到了错误。我正在使用包nnet，并验证ROC曲线。
Error in text.default(DAT[id, 3, i], DAT[id, 2, i], plot.thres[id2], pos = 4, :
零长度＆＃39;标签＆＃39;指定的
中华民国的守则是：
roc.plot(yelp_test$open == "1", outsample_nnet)
roc.plot(yelp_test$open == "1", outsample_nnet)$roc.vol
Out示例代码为：
outsample_nnet<- predict(Yelp_net, yelp_test, type="raw")
outsample_nnet <- outsample_nnet > 0.5
outsample_nnet <- as.numeric(outsample_nnet)
nnets_table <- table(yelp_test$open, outsample_nnet, dnn = c("Truth", "Predicted"))
30 obs的输入代码是： newdata＆lt; - cleanDataFrame [2:10] set.seed（10） （newdata [sample（1：nrow（newdata），30），]）
structure(list(open = c(1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L,
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L,
1L, 1L, 1L, 0L), review_count = c(3L, 5L, 6L, 38L, 6L, 4L, 5L,
23L, 19L, 3L, 22L, 74L, 15L, 38L, 88L, 26L, 9L, 3L, 58L, 4L,
13L, 117L, 38L, 10L, 5L, 6L, 102L, 108L, 264L, 103L), stars = c(3,
4, 4.5, 4, 3, 3, 3, 4, 3.5, 3.5, 3.5, 4.5, 4.5, 4, 2.5, 3.5,
3.5, 3.5, 4, 3, 4.5, 4.5, 4, 3.5, 4, 3.5, 4, 3, 3.5, 4), Freq = c(166L,
12L, 166L, 15L, 45L, 166L, 66L, 79L, 33L, 58L, 150L, 389L, 150L,
1L, 389L, 20L, 389L, 389L, 389L, 166L, 74L, 0L, 389L, 32L, 389L,
161L, 126L, 389L, 98L, 3L), avgRev = c(23.7904191616766, 18.7692307692308,
23.7904191616766, 98, 78.804347826087, 23.7904191616766, 31.3283582089552,
64.3375, 23.1764705882353, 23.6949152542373, 60.6490066225166,
34.1923076923077, 60.6490066225166, 22, 34.1923076923077, 33.1904761904762,
34.1923076923077, 34.1923076923077, 34.1923076923077, 30.8443113772455,
27.6533333333333, 117, 34.1923076923077, 30.4545454545455, 34.1923076923077,
37.2716049382716, 47.3149606299213, 34.1923076923077, 64.3838383838384,
73.75), avgStar = c(3.53592814371257, 3.92307692307692, 3.53592814371257,
3.96875, 3.6195652173913, 3.53592814371257, 3.69402985074627,
3.58125, 3.5, 3.67796610169492, 3.63245033112583, 3.5551282051282,
3.63245033112583, 4, 3.5551282051282, 3.78571428571429, 3.5551282051282,
3.5551282051282, 3.5551282051282, 3.48203592814371, 3.72666666666667,
4.5, 3.5551282051282, 3.65151515151515, 3.5551282051282, 3.43827160493827,
3.63385826771654, 3.5551282051282, 3.60606060606061, 4.25), count = c(4L,
2L, 5L, 5L, 0L, 2L, 5L, 0L, 2L, 8L, 3L, 15L, 4L, 3L, 15L, 14L,
1L, 1L, 0L, 1L, 2L, 0L, 0L, 50L, 1L, 27L, 4L, 51L, 36L, 14L),
recession = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 1L), duration = c(332L, 427L, 614L, 117L, 1894L,
1346L, 140L, 1909L, 1100L, 1030L, 1666L, 2096L, 1054L, 352L,
2145L, 1018L, 1763L, 391L, 2116L, 1567L, 693L, 674L, 1626L,
301L, 295L, 378L, 649L, 376L, 1028L, 2390L)), .Names = c("open",
"review_count", "stars", "Freq", "avgRev", "avgStar", "count",
"recession", "duration"), row.names = c(1439L, 870L, 1210L, 1962L,
242L, 639L, 777L, 771L, 1741L, 1214L, 1840L, 1603L, 322L, 1681L,
1010L, 1209L, 148L, 745L, 1124L, 2354L, 2433L, 1731L, 2180L,
1000L, 1141L, 1985L, 2814L, 674L, 2163L, 999L), class = "data.frame")
此样本中的衰退变量不包含任何1。我随意在最后一次观察中添加了1，以确保代码有效。