.. _metrics_H2OGLRMMetrics: H2OGLRMMetrics Class -------------------- The class provides all metrics available for ``H2OGLRM``. **Getter Methods** getCatCnt() **Returns:** Number of Non-Missing Categorical Values. *Scala type:* ``Long``, *Python type:* ``int``, *R type:* ``integer`` getCatErr() **Returns:** Misclassification Error (Categorical Cols). *Scala type:* ``Double``, *Python type:* ``float``, *R type:* ``numeric`` getCustomMetricName() **Returns:** Name of custom metric. *Scala type:* ``String``, *Python type:* ``string``, *R type:* ``character`` getCustomMetricValue() **Returns:** Value of custom metric. *Scala type:* ``Double``, *Python type:* ``float``, *R type:* ``numeric`` getDataFrameSerializer() **Returns:** A full name of a serializer used for serialization and deserialization of Spark DataFrames to a JSON value within NullableDataFrameParam. *Scala type:* ``String``, *Python type:* ``string``, *R type:* ``character`` getDescription() **Returns:** Optional description for this scoring run (to note out-of-bag, sampled data, etc.). *Scala type:* ``String``, *Python type:* ``string``, *R type:* ``character`` getMSE() **Returns:** The Mean Squared Error of the prediction for this scoring run. *Scala type:* ``Double``, *Python type:* ``float``, *R type:* ``numeric`` getNobs() **Returns:** Number of observations. *Scala type:* ``Long``, *Python type:* ``int``, *R type:* ``integer`` getNumCnt() **Returns:** Number of Non-Missing Numeric Values. *Scala type:* ``Long``, *Python type:* ``int``, *R type:* ``integer`` getNumErr() **Returns:** Sum of Squared Error (Numeric Cols). *Scala type:* ``Double``, *Python type:* ``float``, *R type:* ``numeric`` getRMSE() **Returns:** The Root Mean Squared Error of the prediction for this scoring run. *Scala type:* ``Double``, *Python type:* ``float``, *R type:* ``numeric`` getScoringTime() **Returns:** The time in mS since the epoch for the start of this scoring run. *Scala type:* ``Long``, *Python type:* ``int``, *R type:* ``integer``