multilabel_confusion_matrix: Compute the confusion matrix for a multi-label prediction
In utiml: Utilities for Multi-Label Learning

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)

`mdata`	A mldr dataset
`mlresult`	A mlresult prediction

A mlconfmat object that contains:

Z: The bipartition matrix prediction.
Fx: The score/probability matrix prediction.
R: The ranking matrix prediction.
Y: The expected matrix bipartition.
TP: The True Positive matrix values.
FP: The False Positive matrix values.
TN: The True Negative matrix values.
FN: The False Negative matrix values.
Zi: The total of positive predictions for each instance.
Yi: The total of positive expected for each instance.
TPi: The total of True Positive predictions for each instance.
FPi: The total of False Positive predictions for each instance.
TNi: The total of True Negative predictions for each instance.
FNi: The total False Negative predictions for each instance.
Zl: The total of positive predictions for each label.
Yl: The total of positive expected for each label.
TPl: The total of True Positive predictions for each label.
FPl: The total of False Positive predictions for each label.
TNl: The total of True Negative predictions for each label.
FNl: The total False Negative predictions for each label.

Other evaluation: cv(), multilabel_evaluate(), multilabel_measures()

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