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Parameter max_models does not work properly sometimes #16376
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Is it possible that the extra models are just Stacked Ensembles? Stacked Ensembles are not included in the |
There are only 3 Stacked Ensembles. I attached screenshots in the description. |
Thanks, that does look like a bug. Would you be able to provide us with logs? |
I attached the logs in the descrption. |
Thank you @baobabtree! |
H2O version, Operating System and Environment
3.46.0.3,
Windows 10, R 4.4.1
wsl2 Ubuntu 24.04 (Windows subsystem for Linux), R 4.4.1, Rstudio Server
I tried on both Windows and wsl2.
Actual behavior
When I set the max_models = 20, usually I got 23 models including 3 Stack Ensembles. However, sometimes I got over 40 models.
When I set the max_models = 30, again, sometimes I got over 60 models.
The models are not evenly distributed.
Sometimes most of the models are GBM.
Sometimes most of the models are DeepLearning.
It varies from time to time.
If I set max_runtime_secs and the time spent passed the preset number, there will be no Stack Ensembles.
Screenshots
I set the max_models = 30 and got 53 models in total.
fit <- h2o.automl(x = x, y = y, training_frame = as.h2o(yx), leaderboard_frame = newyx, nfolds = -1,
weights_column = NULL, max_models = 30, stopping_metric = "AUTO", exclude_algos = c("DRF"),
exploitation_ratio = -1, keep_cross_validation_predictions = T, sort_metric = "AUTO",
monotone_constraints = mono)
I attached the logs. This time I got 62 models in total
logs.zip
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