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如何使用插入符号和glmnet应用套索逻辑回归?

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我想重复以下代码行:

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)

...使用插入符号包,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")

参数“alpha”无法识别,“模型拟合在每次折叠中都失败” .

我究竟做错了什么?非常感谢帮助 . 谢谢 .

1 回答

  • 4

    尝试使用tuneGrid . 例如如下:

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

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