逻辑回归的交叉验证和套索正则化错误

Question

我想使用 lasso 正则化创建一个 5 倍 CV 逻辑回归模型，但我收到以下错误消息：Something is wrong; all the RMSE metric values are missing:。

我通过设置alpha=1 开始使用套索正则化逻辑回归。这行得通。我从this example 扩展。

# Load data set
data("mtcars")

# Prepare data set 
x   <- model.matrix(~.-1, data= mtcars[,-1])
mpg <- ifelse( mtcars$mpg < mean(mtcars$mpg), 0, 1)
y   <- factor(mpg, labels = c('notEfficient', 'efficient'))

#find minimum coefficient
mod_cv <- cv.glmnet(x=x, y=y, family='binomial', alpha=1)

#logistic regression with lasso regularization
logistic_model <- glmnet(x, y, alpha=1, family = "binomial",
                         lambda = mod_cv$lambda.min)

我读到glmnet 函数已经做了 10 倍 cv。但我想使用 5-fold cv。因此，当我使用n_folds 将修改添加到cv.glmnet 时，我无法找到最小系数，也无法在修改trControl 时制作模型。

#find minimum coefficient by adding 5-fold cv
mod_cv <- cv.glmnet(x=x, y=y, family='binomial', alpha=1, n_folds=5)


#Error in glmnet(x, y, weights = weights, offset = offset, #lambda = lambda,  : 
#  unused argument (n_folds = 5)

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

# train the model with 5-fold cv
model <- train(x, y, trControl = train_control, method = "glm", family="binomial", alpha=1)

#Something is wrong; all the Accuracy metric values are missing:
#    Accuracy       Kappa    
#Min.   : NA   Min.   : NA  
# 1st Qu.: NA   1st Qu.: NA  
# Median : NA   Median : NA  
# Mean   :NaN   Mean   :NaN  
# 3rd Qu.: NA   3rd Qu.: NA  
# Max.   : NA   Max.   : NA  
 # NA's   :1     NA's   :1  

为什么我添加 5-fold cv 时会出现错误？

Answer 1

您的代码中有两个问题： 1) cv.glmnet 中的 n_folds 参数实际上称为 nfolds 和 2) train 函数不接受 alpha 参数。如果你修复了这些，你的代码就可以工作：

# Load data set
data("mtcars")
library(glmnet)
library(caret)

# Prepare data set 
x   <- model.matrix(~.-1, data= mtcars[,-1])
mpg <- ifelse( mtcars$mpg < mean(mtcars$mpg), 0, 1)
y   <- factor(mpg, labels = c('notEfficient', 'efficient'))

#find minimum coefficient
mod_cv <- cv.glmnet(x=x, y=y, family='binomial', alpha=1)

#logistic regression with lasso regularization
logistic_model <- glmnet(x, y, alpha=1, family = "binomial",
                         lambda = mod_cv$lambda.min)



#find minimum coefficient by adding 5-fold cv
mod_cv <- cv.glmnet(x=x, y=y, family='binomial', alpha=1, nfolds=5)


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

# train the model with 5-fold cv
model <- train(x, y, trControl = train_control, method = "glm", family="binomial")
model$results
#>  parameter  Accuracy     Kappa AccuracySD   KappaSD
#>1      none 0.8742857 0.7362213 0.07450517 0.1644257