title: How can I get Gini index after doing a grid search in hyper parameter tuning of GBM for a tweedie loss function?
body: <p>I am doing hyperparameter tuning for gbm model in H2o and since my loss function is Tweedie I don't want to look at mse as my model selection criteria.</p>
  
  <p>In H2o documentation, it says that Gini index can be calculated for both regression and classification models, however when I try to get it for my Tweedie regression model, it returns null. Below is how I get the best model and score it on the test set.</p>
  
  <pre><code>gbm_sorted_grid &lt;- h2o.getGrid(grid_id = "grid_hp4", sort_by = 
  "residual_deviance")
  
  best_model &lt;- h2o.getModel(gbm_sorted_grid@model_ids[[1]])  
  
  perf &lt;- h2o.performance(best_model, newdata = lrs_test)
  h2o.giniCoef(perf)
  Null
  </code></pre>
  
  <p>And when I try the code below, I get below error:</p>
  
  <pre><code>h2o.giniCoef(best_model) : No Gini for H2ORegressionModel
  </code></pre>
  
  <p>Does this only work on Bernoulli distribution?</p>