r - 插入符号预测目标变量 nrow() 为 Null

Question

东风：

library(caret)

a = c("aa", "bb", "cc", "aa", "aa", "aa", "bb", "cc", "bb", "bb") 
b = c("aa", "bb", "cc", "aa", "aa", "aa", "bb", "cc", "bb", "bb") 
c = c("aa", "bb", "cc", "aa", "aa", "aa", "bb", "cc", "bb", "bb") 
d = c("aa", "bb", "cc", "aa", "aa", "aa", "bb", "cc", "bb", "bb") 
e = c(1, 0, 1, 0, 0, 0, 1, 1, 1, 1)

#df1
df1 = data.frame(a,b,c,d,e)
#df2
df2 = data.frame(a,b,c,d,e)

Caret Log-红色模型：

df1$e <- as.factor(df1$e)
df2$e <- as.factor(df2$e)

# define training control
train_control <- trainControl(method = "cv", number = 5)

# train the model on training set
model <- train(e ~ .,
               data = df1,
               trControl = train_control,
               method = "glm",
               family=binomial())

# logistic <- glm(WonLost ~ . -PANum, data=train, family="binomial")
df2$predict <- caret::predict.train(model, newdata=df2,type = "prob")


nrow(df2$predict)
nrow(df2$e)

为什么 nrow(df2$e) 为零？我根据之前遇到的错误将目标变量更改为一个因子，但这似乎导致了我当前的问题。

警告消息： 1：在 train.default(x, y, weights = w, ...) 中：您正在尝试进行回归并且您的结果只有两个可能的值您是否正在尝试进行分类？如果是这样，请使用 2 水平因子作为结果列。

标签： rr-caret

Answer 1

有时caret对变量很敏感，即使您的glmlogit 模型在回归或分类方面存在问题。我学到的一个建议是将目标变量重新编码为是/否。另外，请注意插入符号的预测被添加为新数据帧，df2这就是为什么nrow()有效，而e只是一个向量，所以你必须使用length()or NROW()。这里的代码：

library(caret)
#Vectors
a = c("aa", "bb", "cc", "aa", "aa", "aa", "bb", "cc", "bb", "bb") 
b = c("aa", "bb", "cc", "aa", "aa", "aa", "bb", "cc", "bb", "bb") 
c = c("aa", "bb", "cc", "aa", "aa", "aa", "bb", "cc", "bb", "bb") 
d = c("aa", "bb", "cc", "aa", "aa", "aa", "bb", "cc", "bb", "bb") 
e = c(1, 0, 1, 0, 0, 0, 1, 1, 1, 1)

#df1
df1 = data.frame(a,b,c,d,e)
#df2
df2 = data.frame(a,b,c,d,e)
#Format
df1$e[df1$e==1] <- 'Yes'
df1$e[df1$e==0] <- 'No'
df2$e[df2$e==1] <- 'Yes'
df2$e[df2$e==0] <- 'No'

# define training control
train_control <- trainControl(method = "cv", number = 5)

# train the model on training set
model <- train(e ~ .,
               data = df1,
               trControl = train_control,
               method = "glm",
               family=binomial())

#Predict
df2$predict <- caret::predict.train(model, newdata=df2,type = "prob")
#Checks
nrow(df2$predict)
NROW(df2$e)
length(df2$e)

输出：

df2
    a  b  c  d   e   predict.No predict.Yes
1  aa aa aa aa Yes 7.500000e-01        0.25
2  bb bb bb bb  No 2.500000e-01        0.75
3  cc cc cc cc Yes 8.646869e-09        1.00
4  aa aa aa aa  No 7.500000e-01        0.25
5  aa aa aa aa  No 7.500000e-01        0.25
6  aa aa aa aa  No 7.500000e-01        0.25
7  bb bb bb bb Yes 2.500000e-01        0.75
8  cc cc cc cc Yes 8.646869e-09        1.00
9  bb bb bb bb Yes 2.500000e-01        0.75
10 bb bb bb bb Yes 2.500000e-01        0.75

nrow(df2$predict)
[1] 10
NROW(df2$e)
[1] 10
length(df2$e)
[1] 10