Description Usage Arguments Details Value See Also Examples
Evaluation metrics based on simple metrics for the confusion matrix, averaged through several criteria.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | accuracy(true_labels, predicted_labels, undefined_value = "diagnose")
precision(true_labels, predicted_labels, undefined_value = "diagnose")
micro_precision(true_labels, predicted_labels, ...)
macro_precision(true_labels, predicted_labels,
undefined_value = "diagnose")
recall(true_labels, predicted_labels, undefined_value = "diagnose")
micro_recall(true_labels, predicted_labels, ...)
macro_recall(true_labels, predicted_labels, undefined_value = "diagnose")
fmeasure(true_labels, predicted_labels, undefined_value = "diagnose")
micro_fmeasure(true_labels, predicted_labels, ...)
macro_fmeasure(true_labels, predicted_labels,
undefined_value = "diagnose")
|
true_labels |
Matrix of true labels, columns corresponding to labels and rows to instances. |
predicted_labels |
Matrix of predicted labels, columns corresponding to labels and rows to instances. |
undefined_value |
The value to be returned when a computation results in an undefined value due to a division by zero. See details. |
... |
Additional parameters for precision, recall and Fmeasure. |
Available metrics in this category
accuracy
: Bipartition based accuracy
fmeasure
: Example and binary partition F_1 measure (harmonic mean between precision and recall, averaged by instance)
macro_fmeasure
: Label and bipartition based F_1 measure (harmonic mean between precision and recall, macro-averaged by label)
macro_precision
: Label and bipartition based precision (macro-averaged by label)
macro_recall
: Label and bipartition based recall (macro-averaged by label)
micro_fmeasure
: Label and bipartition based F_1 measure (micro-averaged)
micro_precision
: Label and bipartition based precision (micro-averaged)
micro_recall
: Label and bipartition based recall (micro-averaged)
precision
: Example and bipartition based precision (averaged by instance)
recall
: Example and bipartition based recall (averaged by instance)
Deciding a value when denominators are zero
Parameter undefined_value
: The value to be returned when a computation
results in an undefined value due to a division by zero. Can be a single
value (e.g. NA, 0), a function with the following signature:
function(tp, fp, tn, fn)
or a string corresponding to one of the predefined strategies. These are:
"diagnose"
: This strategy performs the following decision:
Returns 1 if there are no true labels and none were predicted
Returns 0 otherwise
This is the default strategy, and the one followed by MULAN.
"ignore"
: Occurrences of undefined values will be ignored when
averaging (averages will be computed with potentially less values than
instances/labels). Undefined values in micro-averaged metrics cannot be
ignored (will return NA
).
"na"
: Will return NA
(with class numeric
) and it
will be propagated when averaging (averaged metrics will potentially return
NA
).
Atomical numeric vector containing the resulting value in the range [0, 1].
Other evaluation metrics: Basic metrics
,
Ranking-based metrics
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | true_labels <- matrix(c(
1,1,1,
0,0,0,
1,0,0,
1,1,1,
0,0,0,
1,0,0
), ncol = 3, byrow = TRUE)
predicted_labels <- matrix(c(
1,1,1,
0,0,0,
1,0,0,
1,1,0,
1,0,0,
0,1,0
), ncol = 3, byrow = TRUE)
precision(true_labels, predicted_labels, undefined_value = "diagnose")
macro_recall(true_labels, predicted_labels, undefined_value = 0)
macro_fmeasure(
true_labels, predicted_labels,
undefined_value = function(tp, fp, tn, fn) as.numeric(fp == 0 && fn == 0)
)
|
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