select * from mldb.H2OPredict(
on (select top 10 * from iris_input)
on (select model_id, model, h2o_lic.license from h2o_models where model_id = 'h2odai_iris_model') DIMENSION
using
Accumulate('id')
ModelType('DAI')
) as td;
*** Query completed. 10 rows found. 3 columns returned.
*** Total elapsed time was 1 second.
The following example is a snippet of the total output:
id prediction json_report
----------- -------------------------
17 {"species.1":0.019571473344290463,"species.0":0.9696791626595801,"species.2":0.01074936399612938}
34 {"species.1":0.019504773948357475,"species.0":0.9697824957862801,"species.2":0.010712730265362513}
13 {"species.1":0.019504773948357475,"species.0":0.9697824957862801,"species.2":0.010712730265362513}
32 {"species.1":0.019571473344290463,"species.0":0.9696791626595801,"species.2":0.01074936399612938}
...