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I have two potential use cases where I'd like to fix a single parameter value, but otherwise get the full default parameter grid. If I understand correctly, currently I'd have to specify that full grid in quite verbose JSON, which is a bit much.
For the data I have (lots of dummy variables), both linear and GBDT models do well and, perhaps depending on the downsampling stochastics, sometimes I get a linear model as best, sometimes GBDT. I'd like to fix that so that only linear models are tried (or only GBDT), because I don't want a discontinuity in production of having now a linear model, a month later GBDT, then linear again, etc.
If I oversample (which I'm not currently doing), I believe I need to fix the min_examples_per_node to more than the upsampling replication count. I'd like to set that but otherwise get the default grid.
If you want to keep the CLI simple, having these via Python (#12) would be fine for me too.
The text was updated successfully, but these errors were encountered:
For more complex cases like your example with min_examples_per_node, I think that will be best supported by training directly from python, when #12 is implemented.
I have two potential use cases where I'd like to fix a single parameter value, but otherwise get the full default parameter grid. If I understand correctly, currently I'd have to specify that full grid in quite verbose JSON, which is a bit much.
min_examples_per_node
to more than the upsampling replication count. I'd like to set that but otherwise get the default grid.If you want to keep the CLI simple, having these via Python (#12) would be fine for me too.
The text was updated successfully, but these errors were encountered: