列车xgboost模型不平衡数据

我是一个使用xgboost软件包的新手,我正在尝试创建一个具有最大准确性的模型,并且敏感性和特异性是平衡的。我的问题是,基地是不平衡的约1:3和预测给我一个非常低的灵敏度。

data(cats,package = "MASS")
prop.table(table(cats$Sex))
        F         M 
0.3263889 0.6736111 

library(ggplot2)
ggplot(cats, aes(Bwt, Hwt, colour = Sex, shape = Sex)) + geom_point(size = 3)

加载xgboost包

library(xgboost)

# Split data
set.seed(123);index <- sample(nrow(cats),size = nrow(cats)*0.75)
train <- cats[index,]; test <- cats[-index,]

train_x <- train; test_x <- test
train_x$Sex <- NULL; test_x$Sex <- NULL

# Convert predict variable in numeric
y_train <- as.numeric(train$Sex) - 1; y_test <- as.numeric(test$Sex) - 1
train_x[] <- sapply(train_x, as.numeric); test_x[] <- sapply(test_x, as.numeric)

# Construct xgb.DMatrix object from either a dense matrix
dtrain <- xgb.DMatrix(as.matrix(train_x),label = y_train)
dtest <- xgb.DMatrix(as.matrix(test_x))

## xgboost parameters
xgb_params <- list(booster = "gbtree" 
                , objectve = "binary:logistic"
                , eta=0.1 #default 0.3 [0,1]
                , gamma=0
                , max_depth=7 # default 6 Typical values: 3-10
                , subsample=1
                , tree_method = "exact"
                , scale_pos_weight = 5
                , base_score=median(y_train)
                , seed = 2018)

# tuning Cross Validation 
xgbcv <- xgb.cv(params = xgb_params
                , data = dtrain
                , nrounds = 2000
                , nfold = 7
                , print_every_n = 5
                , early_stopping_rounds = 40
                , maximize = F
                , prediction = F
                , showsd = T
                , metrics = "error")

# train model
gb_dt <- xgb.train(params = xgb_params
                , data = dtrain
                , nrounds = xgbcv$best_iteration
                , print_every_n = 2
                , early_stopping_rounds = 40
                , maximize = F
                , watchlist = list(train=dtrain))

test_probs <- predict(gb_dt, dtest, type = "response")
test_preds <- as.numeric(test_probs > .5)

# Change predicted values to match original data set, check accuracy
test_submit <- 0
test_submit[test_preds==0] <- "F"
test_submit[test_preds==1] <- "M"

我计算混乱矩阵

caret::confusionMatrix(as.factor(test_submit), test$Sex)
Confusion Matrix and Statistics

          Reference
Prediction  F  M
         F  7  0
         M  7 22

               Accuracy : 0.8056          
                 95% CI : (0.6398, 0.9181)
    No Information Rate : 0.6111          
    P-Value [Acc > NIR] : 0.01065         

                  Kappa : 0.55            
 Mcnemar's Test P-Value : 0.02334         

            Sensitivity : 0.5000          
            Specificity : 1.0000          
         Pos Pred Value : 1.0000          
         Neg Pred Value : 0.7586          
             Prevalence : 0.3889          
         Detection Rate : 0.1944          
   Detection Prevalence : 0.1944          
      Balanced Accuracy : 0.7500          

       'Positive' Class : F 

scale_pos_weight 我不知道。欢迎任何建议。 注: 我的兴趣是有一个最佳平衡的模型。尽可能正确地分类最大数量的雌猫。