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When the number of available samples is relatively low, while a lot of features are included the glm might throw warnings, you can get strange output (negative R2 values), ...
Catching these and providing info on what to do could be useful
The text was updated successfully, but these errors were encountered:
When the number of available samples is relatively low, while a lot of features are included the glm might throw warnings, you can get strange output (negative R2 values), ...
Catching these and providing info on what to do could be useful
The text was updated successfully, but these errors were encountered: