<p>The large difference between AUC and AUCPR is most likely caused, as you suggest, by the class imbalance. You can either try to set <code>balance_classes = True</code> or set weights to a column that would weight the minority class more, e.g. taking the inverse of the class frequency. If you have really small number of observations for the minority class, you can try to synthesise more using e.g. <a href="https://arxiv.org/pdf/1106.1813.pdf" rel="nofollow noreferrer">SMOTE</a>.</p>
h2o AutoML use in class imbalance mode
<p>i have a usecase of very imbalace data set , i undersampled the training dataset ,
and tried running the automl in h2o, but it gave me great AUC results (over 0.99) but very bad aup_pr results (0.09).
is it related to the imbalance issue ? 
i ran with weight_column option (<a href="http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/weights_column.html" rel="nofollow noreferrer">http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/weights_column.html</a>) 
but it didn't help.
should i use the balance_classes option instead (when i run both options it fails with "h2oFrame is empty" message) .
the train and test are splitted on date time range , and the test dataset has the proper ration between majority and minority classes. </p>