Research any topic before you write.

Find related topics. | Discover entities. | See connections. | Build a topical map.

F-score

In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all samples predicted to be positive, including those not…

Applications, Criticism & Definition

Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.

Research this topic

Explore the main themes, entities and connections around F-score. Start with the topic map, then use the sections below for research and deeper semantic analysis.

Explore this topic

Start with a few of the strongest sections from the source topic. These are research directions, not a list of keywords you must use.

Topics to explore

Browse the full topic structure. Each item opens a new analysis centered on that subject.

Overview

Etymology

Definition

Applications

Properties

Criticism

Difference from Fowlkes–Mallows index

Extension to multi-class classification

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

Map overview Semantic statistics

F-score

Nodes38
Edges37
Triples21
Avg. degree1.95
Density0.052632
Components1

How this topic connects Entity context

See the strongest relationship patterns around the current topic before diving into the raw triples.

F-score

Top relations

related to Definition · 7
F-score → F-measure, F1, FN, FP, The, TP, With
has application · 4
F-score → Earlier, F1, It, The F-score
related to Dependence of the F-score on class imbalance · 4
F-score → One, Precision-recall, Siblini, This
related to Extension to multi-class classification · 2
F-score → Multiclass, The F-score

Important terminology Word statistics

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

recall precision f1 score positive classification displaystyle class mean beta performance harmonic binary used f-measure true also thus metric value

Entity relationships Subject–Predicate–Object triples

SubjectPredicateObjectConfidenceSrc
the Matthews correlation coefficientinstance ofhence measures0.80text
Informedness or Cohen's kappa may be preferred to assess the performance of a binary classifier.The F-score has been widely used in the natural language processing literatureinstance ofhence measures0.80text
such as in the evaluation of named entity recognitioninstance ofhence measures0.80text
word segmentationinstance ofhence measures0.80text
F-scorehas applicationThe F-score0.60section
F-scorehas applicationIt0.60section
F-scorehas applicationEarlier0.60section
F-scorehas applicationF10.60section
F-scorerelated to DefinitionThe0.60section
F-scorerelated to DefinitionF-measure0.60section
F-scorerelated to DefinitionF10.60section
F-scorerelated to DefinitionWith0.60section

Related concept clusters Concept neighborhoods

These clusters group vocabulary that occurs around closely connected concepts in the source material.

    Connections between topic areas Semantic bridges

    Bridges can reveal useful research angles that are easy to miss in a flat list of related terms.

    Min side: 3
    For writers, content strategists, SEOs, marketers and creators — from quick topic research to advanced semantic analysis.