Description Usage Arguments Details Value Examples
Method for computing Precision, Recall, Specificity, Accuracy and F-measure for multiclass and multilabel classification.
1 2 3 4 | F.measure.multilabel(target, predicted, b.per.example = FALSE)
## S4 method for signature 'matrix,matrix'
F.measure.multilabel(target, predicted, 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 discrete predicted values: rows correspond to examples and columns to classes. predicted[i,j]=1 if example i is predicted belonging to class j, target[i,j]=0 otherwise. |
b.per.example |
a boolean value.
|
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 average precision (P), recall (R),
specificity (S), F-measure (F), average F-measure (avF) and Accuracy (A) across examples. F is the F-measure computed as the
harmonic mean between the average precision and recall; av.F is the F-measure computed as average across examples;
b.per.example==FALSE
: a list with two elements:
average: a named vector with average precision (P), recall (R), specificity (S), F-measure (F), average F-measure (avF) and Accuracy (A) across examples;
per.example: a named matrix with the Precision (P), Recall (R), Specificity (S), Accuracy (A), F-measure (F) and av.F-measure (av.F) for each example. Row names correspond to examples, column names correspond respectively to Precision (P), Recall (R), Specificity (S), Accuracy (A), F-measure (F) and av.F-measure (av.F);
1 2 3 4 5 6 7 8 9 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.