Question

我正在使用决策树对案件进行分类。因为我的数据不平衡，所以我重复了少数派的课程，直到达到50:50的平衡。我确实知道，这是一种非常不寻常的方法，我也尝试了SMOTE函数。

假设我有5％的坏案例，然后是95％的好案例。我重复了不良数据，直到有50％的不良和50％的不良。代码如下。

  #Count frequency of groups
  tab <- table(train$case)

  #Count number of rows to be added
  no_of_rows <- max(tab) - min(tab)

  #count number of rows which are already there in the dataframe for the minimum group
  existing_rows <- which(train$case%in% names(which.min(tab)))

  #Add new rows
  new_df <- rbind(train, train[rep(existing_rows,no_of_rows/length(existing_rows)), ])
  train <- new_df

  #Check the count
  table(train$case)

>   table(train$case)

    bad           good
  15316          15855

现在我要进行60:40的拆分。这意味着60％的坏案例和40％的好案例，但是我不知道该怎么做。

有人可以帮忙吗？谢谢。

Answer 1

您可以阅读此示例。

train <- data.frame(id = 1:31171, case = c(rep("bad", 15316),
                                           rep("good", 15855))) # a simulation of your train data frame
table(train$case)
#  bad  good 
#15316 15855
prop.table(table(train$case))
#      bad      good 
#0.4913541 0.5086459

# Now you want a new data (say train2) with 60% bad cases and 40% good cases
# First, you have to decide the size of train2 and this task strongly depends on your research question

# But here, let's assume we want to keep all the bad cases
# So all the 15316 bad cases should represent 60% of train2 sample size (instead of 49% in train)
# Therefore, the sample size of train2 should be 25527
15316/0.6
# [1] 25526.67
# This means that you have to add to the 15316 bad cases 10211 good cases
25527-15316
#[1] 10211

# You can now sample those 10211 good cases from the 15855 good cases in train
library(dplyr)
set.seed(1234)
good.cases <- train %>% filter(case == "good") %>% sample_n(10211)

# Now merge the bad cases with the good one you sampled
train2 <- train %>% filter(case == "bad") %>% bind_rows(good.cases)
# Check the distribution of cases
table(train2$case)
#  bad  good 
#15316 10211
prop.table(table(train2$case))
#      bad      good 
#0.5999922 0.4000078

重复少数民族课程以达到60％-40％的关系

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