Description Usage Arguments Value See Also Examples
The multi-label confusion matrix is an object that contains the prediction, the expected values and also a lot of pre-processed information related with these data.
1 | multilabel_confusion_matrix(mdata, mlresult)
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mdata |
A mldr dataset |
mlresult |
A mlresult prediction |
A mlconfmat object that contains:
The bipartition matrix prediction.
The score/probability matrix prediction.
The ranking matrix prediction.
The expected matrix bipartition.
The True Positive matrix values.
The False Positive matrix values.
The True Negative matrix values.
The False Negative matrix values.
The total of positive predictions for each instance.
The total of positive expected for each instance.
The total of True Positive predictions for each instance.
The total of False Positive predictions for each instance.
The total of True Negative predictions for each instance.
The total False Negative predictions for each instance.
The total of positive predictions for each label.
The total of positive expected for each label.
The total of True Positive predictions for each label.
The total of False Positive predictions for each label.
The total of True Negative predictions for each label.
The total False Negative predictions for each label.
Other evaluation:
cv()
,
multilabel_evaluate()
,
multilabel_measures()
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | prediction <- predict(br(toyml), toyml)
mlconfmat <- multilabel_confusion_matrix(toyml, prediction)
# Label with the most number of True Positive values
which.max(mlconfmat$TPl)
# Number of wrong predictions for each label
errors <- mlconfmat$FPl + mlconfmat$FNl
# Examples predict with all labels
which(mlconfmat$Zi == toyml$measures$num.labels)
# You can join one or more mlconfmat
part1 <- create_subset(toyml, 1:50)
part2 <- create_subset(toyml, 51:100)
confmatp1 <- multilabel_confusion_matrix(part1, prediction[1:50, ])
confmatp2 <- multilabel_confusion_matrix(part2, prediction[51:100, ])
mlconfmat <- confmatp1 + confmatp2
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