How do you report percentage accuracy for glmnet logistic regression? I am using glmnet where my dependent variable is binary (class 0, class 1). I want to report percentage accuracy of the model. So I use the predict function for my test dataset. However, the values returned are in decimals instead of being 0 and 1. So I set a threshold of 0.5, meaning if the predicted value > 0.5, I consider it as 1 and if predicted value <= 0.5, I consider it as 0. Next I create a confusion matrix by comparing the predicted and actual values of my test data. From this I find the accuracy. I have pasted my sample code below. I am not sure if this is the right approach for reporting the accuracy percentage for a glmnet model predicting a binary dependent variable.
data <- read.csv('datafile', header=T)
mat  <- as.matrix(data)
X    <- mat[, c(1:ncol(mat)-1)]
y    <- mat[, ncol(mat)] 
fit  <- cv.glmnet(X, y, family="binomial", type.measure="class", alpha=0.1)

t                             <- 0.2*nrow(mat) #20% of data
t                             <- as.integer(t) 
testX                         <- mat[1:t, 1:ncol(mat)-1]
predicted_y                   <- predict(fit, s=0.01, testX, type='response')
predicted_y[predicted_y>0.5]  <- 1
predicted_y[predicted_y<=0.5] <- 0
Yactual                       <- mat[1:t, ncol(mat)]
confusion_matrix              <- ftable(Yactual, predicted_y)
accuracy                      <- 100* (sum(diag(confusion_matrix)) / length(predicted_y))

 A: The predict function for glmnet offers a "class" type that will predict the class rather than the response for binomial logistic regression, eliminating the need for your conditionals.  You could also do the cv.glmnet using the type.measure parameter value "auc" or "class" to produce some validation accuracy measures prior to prediction.  
A: glmnet is designed around a proper accuracy score, the (penalized) deviance.  Summaries of predictive discrimination should use proper scores, not arbitrary classifications that are at odds with costs of false positives and false negatives.  Consider a couple of accepted proper scoring rules: Brier (quadratic) score and logarithmic (deviance-like) score.  You can manipulate the proportion classified correctly in a number of silly ways.  The easiest way to see this is if the prevalence of $Y=1$ is 0.98 you will be 0.98 accurate by ignoring all the data and predicting everyone to have $Y=1$.
Another way to saying all this is that by changing from an arbitrary cutoff of 0.5 to another arbitrary cutoff, different features will be selected.  An improper scoring rule is optimized by a bogus model.
A: A much simpler way of doing this is by using the predict function and finding the mean error:
mean(predicted_y!=Yactual)

