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')
EnableOptions('Contributions')
) as td;
*** Query completed. 10 rows found. 3 columns returned.
*** Total elapsed time was 1 second.
id prediction json_report
------------- -------------------------------------------------------------------------------------------------
78 {"contrib_0_petal_width.0":3.0711243762188496,"contrib_0_petal_width.1":-0.9788261243754017,"species.1":0.3119516326074581,"species.0":0.02460896392120639,"contrib_bias.0":-1.5913935770680885,"contrib_bias.1":-1.4478556622786931,"contrib_bias.2":-1.3977147774964207,"species.2":0.6634394034713355,"contrib_0_petal_width.2":-1.6281934434767158}
116 {"contrib_0_petal_width.0":3.0711243762188496,"contrib_0_petal_width.1":-0.9788261243754017,"species.1":0.02267639045773728,"species.0":0.010271546391597528,"contrib_bias.0":-1.5913935770680885,"contrib_bias.1":-1.4478556622786931,"contrib_bias.2":-1.3977147774964207,"species.2":0.9670520631506652,"contrib_0_petal_width.2":-1.6281934434767158}
93 {"contrib_0_petal_width.0":3.0711243762188496,"contrib_0_petal_width.1":-0.9788261243754017,"species.1":0.9580695536607794,"species.0":0.018694189664843257,"contrib_bias.0":-1.5913935770680885,"contrib_bias.1":-1.4478556622786931,"contrib_bias.2":-1.3977147774964207,"species.2":0.023236256674377332,"contrib_0_petal_width.2":-1.6281934434767158}
...