How to apply lasso logistic regression with caret and glmnet?

glmnet, r, r-caret

Solution

Try to use tuneGrid. For example as follows:

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

Problem

I am trying to repeat the following lines of code: ``` 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) ``` ... with the caret package, but it doesn't work: ``` 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") ``` The parameter "alpha" is not recognized, and "the model fit fails in every fold". What am I doing wrong? Help would be much appreciated. Thanks.

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