| multilabel_confusion_matrix {utiml} | R Documentation |
Compute the confusion matrix for a multi-label prediction
Description
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.
Usage
multilabel_confusion_matrix(mdata, mlresult)
Arguments
mdata |
A mldr dataset |
mlresult |
A mlresult prediction |
Value
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.
See Also
Other evaluation:
cv(),
multilabel_evaluate(),
multilabel_measures()
Examples
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