Question

在我的训练数据集上使用最小二乘拟合线性回归模型可以正常工作。

library(Matrix)
library(tm)
library(glmnet)
library(e1071)
library(SparseM)
library(ggplot2)

trainingData <- read.csv("train.csv", stringsAsFactors=FALSE,sep=",", header = FALSE)
testingData  <- read.csv("test.csv",sep=",", stringsAsFactors=FALSE, header = FALSE)

lm.fit = lm(as.factor(V42)~ ., data = trainingData)
linearMPrediction = predict(lm.fit,newdata = testingData, se.fit = TRUE)
mean((linearMPrediction$fit - testingData[,20:41])^2) 
linearMPrediction$residual.scale

然而，当我尝试在我的训练数据集中拟合脊回归模型时，

x = model.matrix(as.factor(V42)~., data = trainingData) 
y = as.factor(trainingData$V42) 
ridge = glmnet(x, y, family = "multinomial", alpha = 1, lambda.min.ratio = 1e-2)

我对multinomial和binomial发行版都有以下错误。

Error in lognet(x, is.sparse, ix, jx, y, weights, offset, alpha, nobs,  : 
  one multinomial or binomial class has 1 or 0 observations; not allowed

我错过了什么吗？任何评论将不胜感激。顺便提一下，这是我的数据的一部分。

> trainingData$V42[1:50]
 [1] "normal"      "normal"      "neptune"     "normal"      "normal"      "neptune"     "neptune"     "neptune"     "neptune"     "neptune"     "neptune"    
[12] "neptune"     "normal"      "warezclient" "neptune"     "neptune"     "normal"      "ipsweep"     "normal"      "normal"      "neptune"     "neptune"    
[23] "normal"      "normal"      "neptune"     "normal"      "neptune"     "normal"      "normal"      "normal"      "ipsweep"     "neptune"     "normal"     
[34] "portsweep"   "normal"      "normal"      "normal"      "neptune"     "normal"      "neptune"     "neptune"     "neptune"     "normal"      "normal"     
[45] "normal"      "neptune"     "teardrop"    "normal"      "warezclient" "neptune"  

> x
      (Intercept)    V1 V2tcp V2udp V3bgp V3courier V3csnet_ns V3ctf V3daytime V3discard V3domain V3domain_u V3echo V3eco_i V3ecr_i V3efs V3exec V3finger V3ftp
1               1     0     1     0     0         0          0     0         0         0        0          0      0       0       0     0      0        0     0
2               1     0     0     1     0         0          0     0         0         0        0          0      0       0       0     0      0        0     0
3               1     0     1     0     0         0          0     0         0         0        0          0      0       0       0     0      0        0     0
4               1     0     1     0     0         0          0     0         0         0        0          0      0       0       0     0      0        0     0
5               1     0     1     0     0         0          0     0         0         0        0          0      0       0       0     0      0        0     0
6               1     0     1     0     0         0          0     0         0         0        0          0      0       0       0     0      0        0     0
7               1     0     1     0     0         0          0     0         0         0        0          0      0       0       0     0      0        0     0
8               1     0     1     0     0         0          0     0         0         0        0          0      0       0       0     0      0        0     0
9               1     0     1     0     0         0          0     0         0         0        0          0      0       0       0     0      0        0     0
10              1     0     1     0     0         0          0     0         0         0        0          0      0       0       0     0      0        0     0

> y[1:50]
 [1] normal      normal      neptune     normal      normal      neptune     neptune     neptune     neptune     neptune     neptune     neptune     normal     
[14] warezclient neptune     neptune     normal      ipsweep     normal      normal      neptune     neptune     normal      normal      neptune     normal     
[27] neptune     normal      normal      normal      ipsweep     neptune     normal      portsweep   normal      normal      normal      neptune     normal     
[40] neptune     neptune     neptune     normal      normal      normal      neptune     teardrop    normal      warezclient neptune    
22 Levels: back buffer_overflow ftp_write guess_passwd imap ipsweep land loadmodule multihop neptune nmap normal phf pod portsweep rootkit satan smurf spy ... warezmaster

> table(y)
y
           back buffer_overflow       ftp_write    guess_passwd            imap         ipsweep            land      loadmodule        multihop         neptune 
            196               6               1              10               5             710               1               1               2            8282 
           nmap          normal             phf             pod       portsweep         rootkit           satan           smurf             spy        teardrop 
            301           13449               2              38             587               4             691             529               1             188 
    warezclient     warezmaster 
            181               7

Answer 1

你对某些类有单一的观察（比如只有1次观察的ftp_write），这是不允许的（并且在错误中明确说明）。

岭回归模型：glmnet

1 个答案: