F1 score

F-MeasureF-scoreF1F_1 score
In statistical analysis of binary classification, the F 1 score (also F-score or F-measure) is a measure of a test's accuracy.wikipedia
42 Related Articles

Sørensen–Dice coefficient

Czekanowski binary indexDiceDice coefficient
The F 1 score is also known as the Sørensen–Dice coefficient or Dice similarity coefficient (DSC).
F1 score

Binary classification

binarybinary classifierbinary categorization
In statistical analysis of binary classification, the F 1 score (also F-score or F-measure) is a measure of a test's accuracy.
The F-score combines precision and recall into one number via a choice of weighing, most simply equal weighing, as the balanced F-score (F1 score).

Harmonic mean

harmonicweighted harmonic meanharmonic average
The F 1 score is the harmonic average of the precision and recall, where an F 1 score reaches its best value at 1 (perfect precision and recall) and worst at 0.
In computer science, specifically information retrieval and machine learning, the harmonic mean of the precision (true positives per predicted positive) and the recall (true positives per real positive) is often used as an aggregated performance score for the evaluation of algorithms and systems: the F-score (or F-measure).

Named-entity recognition

named entity recognitionentity extractionnamed entities
The F-score has been widely used in the natural language processing literature, such as the evaluation of named entity recognition and word segmentation.
For example, the best system entering MUC-7 scored 93.39% of F-measure while human annotators scored 97.60% and 96.95%.

Youden's J statistic

informednessYouden's index
Note, however, that the F-measures do not take the true negatives into account, and that measures such as the Matthews correlation coefficient, Informedness or Cohen's kappa may be preferable to assess the performance of a binary classifier.
An unrelated but commonly used combination of basic statistics from Information Retrieval is the F-score, being a (possibly weighted) harmonic mean of recall and precision where recall = sensitivity = true positive rate, but specificity and precision are totally different measures.

Precision and recall

precisionrecallF-measure
The F 1 score is the harmonic average of the precision and recall, where an F 1 score reaches its best value at 1 (perfect precision and recall) and worst at 0. It considers both the precision p and the recall r of the test to compute the score: p is the number of correct positive results divided by the number of all positive results returned by the classifier, and r is the number of correct positive results divided by the number of all relevant samples (all samples that should have been identified as positive).
The two measures are sometimes used together in the F1 Score (or f-measure) to provide a single measurement for a system.

C. J. van Rijsbergen

C. J. 'Keith' van RijsbergenC.J. van RijsbergenVan Rijsbergen
It is based on Van Rijsbergen's effectiveness measure
*F1 score

Matthews correlation coefficient

Matthews Correlation
Note, however, that the F-measures do not take the true negatives into account, and that measures such as the Matthews correlation coefficient, Informedness or Cohen's kappa may be preferable to assess the performance of a binary classifier.
F1 score

NIST (metric)

NISTNIST metrics
NIST (metric)
F-Measure

Word error rate

Word error rate (WER)error rateWER model
Word error rate (WER)
F-Measure

Uncertainty coefficient

proficiencyProficiency (metric)Theil's U
Uncertainty coefficient, aka Proficiency
F1 score

Statistics

statisticalstatistical analysisstatistician
In statistical analysis of binary classification, the F 1 score (also F-score or F-measure) is a measure of a test's accuracy.

Type I and type II errors

type I errorfalse positivefalse-positive
The formula in terms of Type I and type II errors:

Information retrieval

queryretrievalqueries
The F-score is often used in the field of information retrieval for measuring search, document classification, and query classification performance.

Web search engine

search enginesearch enginessearch
The F-score is often used in the field of information retrieval for measuring search, document classification, and query classification performance.

Document classification

text classificationtext categorizationtext categorisation
The F-score is often used in the field of information retrieval for measuring search, document classification, and query classification performance.

Web query classification

query classification
The F-score is often used in the field of information retrieval for measuring search, document classification, and query classification performance.

Machine learning

learningmachine-learningstatistical learning
The F-score is also used in machine learning.

Cohen's kappa

kappakappa statisticCohen Kappa
Note, however, that the F-measures do not take the true negatives into account, and that measures such as the Matthews correlation coefficient, Informedness or Cohen's kappa may be preferable to assess the performance of a binary classifier.

Text segmentation

word segmentationsegmentedsegmentation
The F-score has been widely used in the natural language processing literature, such as the evaluation of named entity recognition and word segmentation.

David Hand (statistician)

David HandDavid J. HandD. J. Hand
David Hand and others criticize the widespread use of the F-score since it gives equal importance to precision and recall.