title: Type measure differences in glmnet package?
body: <p>What is the difference between using "mse" and "class" in the glmnet package?</p>
  
  <pre><code>log_x &lt;- model.matrix(response~.,train)
  log_y &lt;- ifelse(train$response=="good",1,0)
  log_cv &lt;- cv.glmnet(log_x,log_y,alpha=1,family="binomial", type.measure =  "class")
  summary(log_cv)
  plot(log_cv)
  </code></pre>
  
  <p>vs. </p>
  
  <pre><code>log_x &lt;- model.matrix(response~.,train)
  log_y &lt;- ifelse(train$response=="good",1,0)
  log_cv &lt;- cv.glmnet(log_x,log_y,alpha=1,family="binomial", type.measure =  "mse")
  summary(log_cv)
  plot(log_cv)
  </code></pre>
  
  <p>I'm noticing that I'm getting a slightly different curve, or smootness in my plot, and a few % difference in accuracy. But for predicting a binnomial class response is one type measure more appropriate than the other?</p>