如何使用 caret 和 glmnet 应用套索逻辑回归？

Question

我正在尝试重复以下代码行：

x.mat <- as.matrix(train.df[,predictors])
y.class <- train.df$Response

cv.lasso.fit <- cv.glmnet(x = x.mat, y = y.class, 
                          family = "binomial", alpha = 1, nfolds = 10)

...使用 caret 包，但它不起作用：

trainControl <- trainControl(method = "cv",
                       number = 10,
                       # Compute Recall, Precision, F-Measure
                       summaryFunction = prSummary,
                       # prSummary needs calculated class probs
                       classProbs = T)

modelFit <- train(Response ~ . -Id, data = train.df, 
            method = "glmnet", 
            trControl = trainControl,
            metric = "F", # Optimize by F-measure
            alpha=1,
            family="binomial")

无法识别参数"alpha"，"the model fit fails in every fold"。

我做错了什么？帮助将不胜感激。谢谢

Answer 1

尝试使用tuneGrid。例如如下：

tuneGrid=expand.grid(
              .alpha=1,
              .lambda=seq(0, 100, by = 0.1))

如何使用 caret 和 glmnet 应用套索逻辑回归？

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