来源 | R友舍

分箱方法是一种简单常用的预处理方法。所谓“分箱”，实际上就是按照属性值划分的子区间，如果一个属性值处于某个子区间范围内，就称把该属性值放进这个子区间所代表的“箱子”内。

不同的数据分析工具(如SAS、SPSS)都有相应的模块来对连续数据进行分箱操作，R语言主要使用smbinning包进行数据分箱操作。

library(smbinning)

## Loading required package: sqldf

## Loading required package: gsubfn

## Loading required package: proto

## Loading required package: RSQLite

## Loading required package: DBI

## Warning: package 'DBI' was built under R version 3.3.2

## Loading required package: partykit

## Loading required package: grid

## Loading required package: Formula

以chileancredit的征信数据为例，可以利用smbinning包实现数据的分箱，先看看数据概要：

head(chileancredit)

##    CustomerId TOB IncomeLevel   Bal01 MaxDqBin01 MaxDqBin02 MaxDqBin03## 9  0000000185  44           1  604.86          0          0          0## 13 0000000238  79           1 1006.21          0          0          0## 21 0000000346 102           1  299.23          0          0          0## 25 0000000460  NA           1  645.19          0          0          0## 31 0000000549 109        <NA>  218.00          0          0          0## 32 0000000559 183        <NA>   10.32          0          0          0##    MaxDqBin04 MaxDqBin05 MaxDqBin06 MtgBal01 NonBankTradesDq01## 9           0          0          0        0                 0## 13          0          0          0        0                 0## 21          0          0          0        0                 0## 25          0          0          0        0                 0## 31          0          0          0        0                 0## 32          0          0          0        0                 0##    NonBankTradesDq02 NonBankTradesDq03 NonBankTradesDq04 NonBankTradesDq05## 9                  0                 0                 0                 0## 13                 0                 0                 0                 0## 21                 0                 0                 0                 0## 25                 0                 0                 0                 0## 31                 0                 0                 0                 0## 32                 0                 0                 0                 0##    NonBankTradesDq06 FlagGB FlagSample## 9                  0      1          1## 13                 0      1          1## 21                 0      1          1## 25                 0      1          1## 31                 0      1          1## 32                 0      1          1

str(chileancredit)

## 'data.frame':    7702 obs. of  19 variables:##  $ CustomerId       : chr  "0000000185" "0000000238" "0000000346" "0000000460" ...##  $ TOB              : int  44 79 102 NA 109 183 172 76 136 171 ...##  $ IncomeLevel      : Factor w/ 6 levels "0","1","2","3",..: 2 2 2 2 NA NA 1 2 1 1 ...##  $ Bal01            : num  605 1006 299 645 218 ...##  $ MaxDqBin01       : Factor w/ 7 levels "0","1","2","3",..: 1 1 1 1 1 1 1 1 1 1 ...##  $ MaxDqBin02       : Factor w/ 8 levels "0","1","2","3",..: 1 1 1 1 1 1 1 1 1 1 ...##  $ MaxDqBin03       : Factor w/ 8 levels "0","1","2","3",..: 1 1 1 1 1 1 1 2 1 1 ...##  $ MaxDqBin04       : Factor w/ 8 levels "0","1","2","3",..: 1 1 1 1 1 1 1 1 1 1 ...##  $ MaxDqBin05       : Factor w/ 8 levels "0","1","2","3",..: 1 1 1 1 1 1 1 1 1 1 ...##  $ MaxDqBin06       : Factor w/ 8 levels "0","1","2","3",..: 1 1 1 1 1 1 1 1 1 1 ...##  $ MtgBal01         : num  0 0 0 0 0 0 0 0 0 0 ...##  $ NonBankTradesDq01: int  0 0 0 0 0 0 0 0 0 0 ...##  $ NonBankTradesDq02: int  0 0 0 0 0 0 0 0 0 0 ...##  $ NonBankTradesDq03: int  0 0 0 0 0 0 0 0 0 0 ...##  $ NonBankTradesDq04: int  0 0 0 0 0 0 0 1 0 0 ...##  $ NonBankTradesDq05: int  0 0 0 0 0 0 0 1 0 0 ...##  $ NonBankTradesDq06: int  0 0 0 0 0 0 0 1 0 0 ...##  $ FlagGB           : int  1 1 1 1 1 1 1 1 1 1 ...##  $ FlagSample       : int  1 1 1 1 1 1 1 1 1 1 ...

对数据集切分为训练集与测试集两类：FlagSample=1作为训练集，另一半作为测试集：

chileancredit.train=subset(chileancredit,FlagSample==1)chileancredit.test=subset(chileancredit,FlagSample==0)

以TOB变量为例，我们可以尝试对其进行分箱处理：

smbinning包的分箱原理是基于构造条件推断树ctree的监督式分享，因此需要提前定义好目标标签Y，这里将用户好坏标签FlagGB作为分箱的目标标签。

result=smbinning(df=chileancredit.train,y="FlagGB",x="TOB",p=0.05)

## Loading required package: tcltk

分好箱后，smbinning包还提供了smbinning.plot函数来将分箱效果给展现出来：

par(mfrow=c(2,2))boxplot(chileancredit.train$TOB~chileancredit.train$FlagGB,        horizontal=T, frame=F, col="lightgray",main="Distribution")mtext("Time on Books (Months)",3)smbinning.plot(result,option="dist",sub="Time on Books (Months)")smbinning.plot(result,option="badrate",sub="Time on Books (Months)")smbinning.plot(result,option="WoE",sub="Time on Books (Months)")

图1与图2是变量的分布情况，图3表示的是bad标签的比例，除了缺失值外，bad标签的比例表现为递减趋势。图4为分箱变量的woe指标。woe指标是评分卡模型里极为常见的变量评价指标，反映了变量区分好坏标签的程度，woe值越大，则说明该组good标签占比越高，反之则说明bad标签占比越高。

最后根据分箱逻辑，smbinning还提供了smbinning.sql函数用以形成sql逻辑：

smbinning.sql(result)

## [1] "case when TOB <= 17 then '01: TOB <= 17' when TOB <= 30 then '02: TOB <= 30' when TOB <= 63 then '03: TOB <= 63' when TOB > 63 then '04: TOB > 63' when TOB Is Null then 'TOB Is Null' else '99: Error' end "

根据分箱结果后，还可以利用smbinning.gen函数，在原始数据集上直接衍生出分箱变量：

chileancredit=smbinning.gen(chileancredit, result, chrname = "gTOB")

smbinning包还提供了IV评价指标。IV也称Information Value,是评价变量对分类标签区分能力的一种指标，常用于评分卡变量评价上。通常IV值越高，则说明变量预测能力越好。评分卡构建过程中，分析人员也更加倾向于选择高IV值，线性关系显著的变量作为预备变量纳入模型筛选集。

sumivt=smbinning.sumiv(chileancredit.train,y="FlagGB")

print(sumivt)

##                 Char     IV                  Process## 5         MaxDqBin01 2.3771        Factor binning OK## 6         MaxDqBin02 1.8599        Factor binning OK## 12 NonBankTradesDq01 1.8129       Numeric binning OK## 13 NonBankTradesDq02 1.4417       Numeric binning OK## 7         MaxDqBin03 1.3856        Factor binning OK## 14 NonBankTradesDq03 1.1819       Numeric binning OK## 8         MaxDqBin04 1.0729        Factor binning OK## 15 NonBankTradesDq04 0.8948       Numeric binning OK## 9         MaxDqBin05 0.8844        Factor binning OK## 16 NonBankTradesDq05 0.7511       Numeric binning OK## 10        MaxDqBin06 0.6302        Factor binning OK## 17 NonBankTradesDq06 0.5501       Numeric binning OK## 2                TOB 0.5025       Numeric binning OK## 3        IncomeLevel 0.3380        Factor binning OK## 11          MtgBal01 0.1452       Numeric binning OK## 4              Bal01 0.1379       Numeric binning OK## 1         CustomerId     NA   Not numeric nor factor## 18        FlagSample     NA Uniques values of x < 10

最后，可以用smbinning.sumiv.plot函数将变量IV值进行排序，并进行可视化展现出来，方便分析人员进行变量筛选。

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