在此处绘制AUC图

Question

我目前正在关注以下link的幻灯片。我在幻灯片121/128上，我想知道如何复制AUC。作者没有解释如何这样做（也在幻灯片124上也是如此）。其次，在幻灯片125上，生成以下代码;

bestRound = which.max(as.matrix(cv.res)[,3]-as.matrix(cv.res)[,4])
bestRound

我收到以下错误;

as.matrix（cv.res）[，2]中的错误：下标越界

以下代码的数据可以从here下载，我已经制作了以下代码供您参考。

问题：如何以作者的身份制作AUC，为什么下标超出界限？

-----代码------

# Kaggle Winning Solutions

train <- read.csv('train.csv', header = TRUE)
test <- read.csv('test.csv', header = TRUE)
y <- train[, 1]
train <- as.matrix(train[, -1])
test <- as.matrix(test)

train[1, ]

#We want to determin who is more influencial than the other

new.train <- cbind(train[, 12:22], train[, 1:11])
train = rbind(train, new.train)
y <- c(y, 1 - y)

x <- rbind(train, test)

(dat[,i]+lambda)/(dat[,j]+lambda)

A.follow.ratio = calcRatio(x,1,2)
A.mention.ratio = calcRatio(x,4,6)
A.retweet.ratio = calcRatio(x,5,7)
A.follow.post = calcRatio(x,1,8)
A.mention.post = calcRatio(x,4,8)
A.retweet.post = calcRatio(x,5,8)
B.follow.ratio = calcRatio(x,12,13)
B.mention.ratio = calcRatio(x,15,17)
B.retweet.ratio = calcRatio(x,16,18)
B.follow.post = calcRatio(x,12,19)
B.mention.post = calcRatio(x,15,19)
B.retweet.post = calcRatio(x,16,19)

x = cbind(x[,1:11],
          A.follow.ratio,A.mention.ratio,A.retweet.ratio,
          A.follow.post,A.mention.post,A.retweet.post,
          x[,12:22],
          B.follow.ratio,B.mention.ratio,B.retweet.ratio,
          B.follow.post,B.mention.post,B.retweet.post)

AB.diff = x[,1:17]-x[,18:34]
x = cbind(x,AB.diff)
train = x[1:nrow(train),]
test = x[-(1:nrow(train)),]

set.seed(1024)
cv.res <- xgb.cv(data = train, nfold = 3, label = y, nrounds = 100, verbose = FALSE,
                 objective = 'binary:logistic', eval_metric = 'auc')

在此处绘制AUC图

set.seed(1024)
cv.res = xgb.cv(data = train, nfold = 3, label = y, nrounds = 3000,
                objective='binary:logistic', eval_metric = 'auc',
                eta = 0.005, gamma = 1,lambda = 3, nthread = 8,
                max_depth = 4, min_child_weight = 1, verbose = F,
                subsample = 0.8,colsample_bytree = 0.8)

以下是我遇到的代码中断

#bestRound: -  subscript out of bounds

bestRound <- which.max(as.matrix(cv.res)[,3]-as.matrix(cv.res)[,4])
bestRound
cv.res

cv.res[bestRound,]

set.seed(1024) bst <- xgboost(data = train, label = y, nrounds = 3000,
                             objective='binary:logistic', eval_metric = 'auc',
                             eta = 0.005, gamma = 1,lambda = 3, nthread = 8,
                             max_depth = 4, min_child_weight = 1,
                             subsample = 0.8,colsample_bytree = 0.8)
preds <- predict(bst,test,ntreelimit = bestRound)

result <- data.frame(Id = 1:nrow(test), Choice = preds)
write.csv(result,'submission.csv',quote=FALSE,row.names=FALSE)

Answer 1

代码的许多部分对我来说没什么意义，但这是使用提供的数据构建模型的最小示例：

数据：

train <- read.csv('train.csv', header = TRUE)
y <- train[, 1]
train <- as.matrix(train[, -1])

型号：

library(xgboost)
cv.res <- xgb.cv(data = train, nfold = 3, label = y, nrounds = 100, verbose = FALSE,
                 objective = 'binary:logistic', eval_metric = 'auc', prediction = T)

要获得交叉验证预测，必须在调用prediction = T时指定xgb.cv。

获得最佳迭代：

it = which.max(cv.res$evaluation_log$test_auc_mean)
best.iter = cv.res$evaluation_log$iter[it]

在交叉验证结果上绘制ROC曲线：

library(pROC)
plot(pROC::roc(response = y,
               predictor = cv.res$pred,
               levels=c(0, 1)),
     lwd=1.5)

获得混淆矩阵（假设0.5 prob是阈值）：

library(caret)
confusionMatrix(ifelse(cv.res$pred <= 0.5, 0, 1), y)
#output
          Reference
Prediction    0    1
         0 2020  638
         1  678 2164

               Accuracy : 0.7607         
                 95% CI : (0.7492, 0.772)
    No Information Rate : 0.5095         
    P-Value [Acc > NIR] : <2e-16         

                  Kappa : 0.5212         
 Mcnemar's Test P-Value : 0.2823         

            Sensitivity : 0.7487         
            Specificity : 0.7723         
         Pos Pred Value : 0.7600         
         Neg Pred Value : 0.7614         
             Prevalence : 0.4905         
         Detection Rate : 0.3673         
   Detection Prevalence : 0.4833         
      Balanced Accuracy : 0.7605         

       'Positive' Class : 0

有人说应该通过交叉验证来调整超参数，例如eta，gamma，lambda，subsample，colsample_bytree，colsample_bylevel等。

最简单的方法是构建一个网格搜索，在所有超参数组合上使用expand.grid，并在网格上使用lapply，xgb.cv作为自定义函数的一部分）。如果您需要更多细节，请发表评论。

从R中的xgboost模型绘制AUC

在此处绘制AUC图

以下是我遇到的代码中断

1 个答案: