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after running automl (classification of 3 classes), I can see a list of models as follows:
model_id mean_per_class_error
StackedEnsemble_BestOfFamily_0_AutoML_20180420_174925 0.262355
StackedEnsemble_AllModels_0_AutoML_20180420_174925 0.262355
XRT_0_AutoML_20180420_174925 0.266606
DRF_0_AutoML_20180420_174925 0.278428
GLM_grid_0_AutoML_20180420_174925_model_0 0.442917
but mean_per_class_error is not a good metric for my case, where classes are unbalanced (one class has very small population). How to fetch details of non-leader models and calculate other metrics? Thanks.
python version: 3.6.0
h2o version: 3.18.0.5
304
asked Apr 24 '18 17:04
H2O AutoML functionalitiesTrains a Random grid of algorithms like GBMs, DNNs, GLMs, etc. using a carefully chosen hyper-parameter space. Individual models are tuned using cross-validation. Two Stacked Ensembles are trained.
September 23, 2022. Databricks AutoML helps you automatically apply machine learning to a dataset. You provide the dataset and identify the prediction target, while AutoML prepares the dataset for model training. AutoML then performs and records a set of trials that creates, tunes, and evaluates multiple models.
actually just figured this out myself (assuming aml is the h2o automl object after training):
for m in aml.leaderboard.as_data_frame()['model_id']:
print(m)
print(h2o.get_model(m))
68
answered Jan 03 '23 02:01
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