title: Can we give a custom metric for cross-validation with GLM in H2O?
body: <p>I am trying to use <code>h2o.glm</code> to find the optimal penalty <code>lambda</code> by cross-validation. This is a multinomial model.</p>
  
  <p>However, I see that it is optimizing according to the multinomial deviance. Can I do cross-validation with respect to some other metric, such as misclassification error? </p>
  
  <p>The parameter <code>custom_metric_func</code> is mentioned in the docs, but I am not clear on its description. Is this metric used as the cross-validation score? If yes, the docs also state that it is only available in the <code>Python</code> API. Is this really true?</p>