r - AUC metrics on XGBoost -

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i build model prediction xgboost:

setdt(train) setdt(test)  labels <- train$goal ts_label <- test$goal new_tr <- model.matrix(~.+0,data = train[,-c("goal"),with=f])  new_ts <- model.matrix(~.+0,data = test[,-c("goal"),with=f])  labels <- as.numeric(labels)-1 ts_label <- as.numeric(ts_label)-1  dtrain <- xgb.dmatrix(data = new_tr,label = labels)  dtest <- xgb.dmatrix(data = new_ts,label=ts_label)  params <- list(booster = "gbtree", objective = "binary:logistic", eta=0.3, gamma=0, max_depth=6, min_child_weight=1, subsample=1, colsample_bytree=1)  xgb1 <- xgb.train(params = params, data = dtrain, nrounds = 291, watchlist = list(val=dtest,train=dtrain), print_every_n = 10,                     early_stop_round = 10, maximize = f , eval_metric = "error")   xgbpred <- predict(xgb1,dtest) xgbpred <- ifelse(xgbpred > 0.5,1,0)  confusionmatrix(xgbpred, ts_label)  confusion matrix , statistics            reference prediction    0    1          0 1904   70          1  191 2015                 accuracy : 0.9376                                 95% ci : (0.9298, 0.9447)          no information rate : 0.5012                    p-value [acc > nir] : < 0.00000000000000022                    kappa : 0.8751                 mcnemar's test p-value : 0.0000000000001104                 sensitivity : 0.9088                            specificity : 0.9664                         pos pred value : 0.9645                         neg pred value : 0.9134                             prevalence : 0.5012                         detection rate : 0.4555                   detection prevalence : 0.4722                      balanced accuracy : 0.9376                        'positive' class : 0

this accuracy suits me, want check metric of auc. write:

xgb1 <- xgb.train(params = params, data = dtrain, nrounds = 291, watchlist = list(val=dtest,train=dtrain), print_every_n = 10,                     early_stop_round = 10, maximize = f , eval_metric = "auc")

but after don't know how make prediction concerning auc metrics. need help, because first experience xgboost. thanks.

upd: far understand, after auc metric need coefficient cut classes. cut off in 0,5

i edit code:

you can directly confussion matrix:

cm<-confusionmatrix(xgbpred, ts_label)$table t = cm[1,1]/(cm[1,1]+cm[2,1]) f = cm[2,2]/(cm[2,1]+cm[2,2])  auc = (1+t-f)/2




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