Comments (5)
This was introduced by me: 738b7bd
Maybe this check is too strict?
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The commit that caused the book failure fixed #943 (comment)
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I debugged this and for the RF_imp_Hist, the levels for the feature "Electrical" are:
-
during train:
id type levels <char> <char> <list> 1: Electrical factor FuseA,FuseF,FuseP,Mix,SBrkr,.MISSING
-
during predict
Key: <id> id type levels <char> <char> <list> 1: Electrical factor FuseA,FuseF,FuseP,Mix,SBrkr
In this case we are lucky that .MISSING is the last level, so dropping it does not change how as.integer()
behaves for the remaining factor levels, but if it was not the last level this would now cause a bug if used in conjunction with lrn("classif.ksvm")
.
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I think the PipeOp
should ensure that either the .MISSING
level is also present during prediction or not present during train @mb706.
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I guess the error message should still be improved
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