Comments (2)
xdavio
commented on May 27, 2024
Thank you for asking this question. As far as I know, the splits in the sklearn cross validator are deterministic by default, which is causing the issue. An immediate workaround is randomizing the dataset row order as a preprocessing step.
An alternative might be to set shuffle to True and pass the random state to the cross validator object right below the referenced line, but there may be a better way to handle this (such as exposing the cv object itself as an init parameter to the estimators). I'll follow up.
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stephen-hoover
commented on May 27, 2024
This was fixed by #24 .
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