find.best.f: Best hierarchical F-score
In HEMDAG: Hierarchical Ensemble Methods for Directed Acyclic Graphs

Description Usage Arguments Details Value Examples

Select the best hierarchical F-score by choosing an appropriate threshold in the scores.

find.best.f(
  target,
  predicted,
  n.round = 3,
  verbose = TRUE,
  b.per.example = FALSE
)

`target`	matrix with the target multilabel: rows correspond to examples and columns to classes. target[i,j]=1 if example i belongs to class j, target[i,j]=0 otherwise.
`predicted`	a numeric matrix with continuous predicted values (scores): rows correspond to examples and columns to classes.
`n.round`	number of rounding digits to be applied to predicted (`default=3`).
`verbose`	a boolean value. If `TRUE` (def.) the number of iterations are printed on stdout.
`b.per.example`	a boolean value. `TRUE`: results are returned for each example; `FALSE`: only the average results are returned;

All the examples having no positive annotations are discarded. The predicted scores matrix (predicted) is rounded according to parameter n.round and all the values of predicted are divided by max(predicted). Then all the thresholds corresponding to all the different values included in predicted are attempted, and the threshold leading to the maximum F-measure is selected.

Names of rows and columns of target and predicted matrix must be provided in the same order, otherwise a stop message is returned.

Two different outputs respect to the input parameter b.per.example:

b.per.example==FALSE: a list with a single element average. A named vector with 7 elements relative to the best result in terms of the F.measure: Precision (P), Recall (R), Specificity (S), F.measure (F), av.F.measure (av.F), Accuracy (A) and the best selected Threshold (T). F is the F-measure computed as the harmonic mean between the average precision and recall; av.F is the F-measure computed as the average across examples and T is the best selected threshold;
b.per.example==FALSE: a list with two elements:
1. average: a named vector with with 7 elements relative to the best result in terms of the F.measure: Precision (P), Recall (R), Specificity (S), F.measure (F), av.F.measure (av.F), Accuracy (A) and the best selected Threshold (T);
2. per.example: a named matrix with the Precision (P), Recall (R), Specificity (S), Accuracy (A), F-measure (F), av.F-measure (av.F) and the best selected Threshold (T) for each example. Row names correspond to examples, column names correspond respectively to Precision (P), Recall (R), Specificity (S), Accuracy (A), F-measure (F), av.F-measure (av.F) and the best selected Threshold (T);

data(graph);
data(labels);
data(scores);
root <- root.node(g);
L <- L[,-which(colnames(L)==root)];
S <- S[,-which(colnames(S)==root)];
fscore <- find.best.f(L, S, n.round=3, verbose=TRUE, b.per.example=TRUE);