train model.r

# Train the model
glmmod = glmnet(x=train_sparse, y=as.factor(train[,2]), alpha=1, family="binomial")
plot(glmmod, xvar="lambda")
glmmod

coef(glmmod)[,100]

# Try cross validation lasso
cv.glmmod = cv.glmnet(x=train_sparse, y=as.factor(train[,2]), alpha=1, family="binomial")
plot(cv.glmmod)

lambda = cv.glmmod$lambda.1se # the value of lambda used by default
lambda

coefs = as.matrix(coef(cv.glmmod)) # convert to a matrix (618 by 1)
ix = which(abs(coefs[,1]) > 0)
length(ix)

coefs[ix,1, drop=FALSE]

test$cv.glmmod <- predict(cv.glmmod,newx=test_sparse,type='response')[,1]

########################

# Get optimal lambda
best.lambda <- cv.glmmod$lambda.min
best.lambda