About: Evaluation of binary classifiers

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The evaluation of binary classifiers compares two methods of assigning a binary attribute, one of which is usually a standard method and the other is being investigated. There are many metrics that can be used to measure the performance of a classifier or predictor; different fields have different preferences for specific metrics due to different goals. For example, in medicine sensitivity and specificity are often used, while in computer science precision and recall are preferred. An important distinction is between metrics that are independent on the prevalence (how often each category occurs in the population), and metrics that depend on the prevalence – both types are useful, but they have very different properties.

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rdfs:label	Beurteilung eines binären Klassifikators (de) Evaluation of binary classifiers (en)
rdfs:comment	The evaluation of binary classifiers compares two methods of assigning a binary attribute, one of which is usually a standard method and the other is being investigated. There are many metrics that can be used to measure the performance of a classifier or predictor; different fields have different preferences for specific metrics due to different goals. For example, in medicine sensitivity and specificity are often used, while in computer science precision and recall are preferred. An important distinction is between metrics that are independent on the prevalence (how often each category occurs in the population), and metrics that depend on the prevalence – both types are useful, but they have very different properties. (en) Bei einer Klassifizierung werden Objekte anhand von bestimmten Merkmalen durch einen Klassifikator in verschiedene Klassen eingeordnet. Der Klassifikator macht dabei im Allgemeinen Fehler, ordnet also in manchen Fällen ein Objekt einer falschen Klasse zu. Aus der relativen Häufigkeit dieser Fehler lassen sich quantitative Maße zur Beurteilung eines Klassifikators ableiten. Ja-Nein-Klassifikationen weisen Ähnlichkeiten zu statistischen Tests auf, bei denen zwischen einer Nullhypothese und einer Alternativhypothese entschieden wird. (de)
foaf:depiction
dcterms:subject	Machine learning Statistical classification
Wikipage page ID	43218024 (xsd:integer)
Wikipage revision ID	1109348568 (xsd:integer)
Link from a Wikipage to another Wikipage	Scoring rule Negative predictive value Neoplasm Prevalence Bayes' theorem Homogeneous function Human chorionic gonadotropin Attributable risk percent Information extraction Information retrieval Correlation Matthews correlation coefficient Geometric mean Confusion matrix Contingency table Receiver Operating Characteristic Likelihood function Phi coefficient Surrogate endpoint Machine learning Grand total Youden's J statistic Expected value False negative False positive Diagnostic odds ratio Gold standard (test) Dual (mathematics) Precision and recall Harmonic mean Attributable risk Accuracy and precision Cohen's kappa Statistical classification Population impact measures Positive predictive value Animal embryonic development Gravid Odds ratio Receiver operating characteristic Sensitivity (tests) Markedness Sensitivity and specificity Test statistic F-score Pre- and post-test probability F1 score True negative True positive Posterior probabilities Summary statistic Scale invariant Antidiagonal Cross tabulation Regression coefficient Prior probabilities Informedness Specificity (tests) Marginal total True Negative Rate True Positive Rate dbr:Homogeneous_linear dbr:Homogeneous_quadratic
sameAs	Evaluation of binary classifiers Evaluation of binary classifiers Evaluation of binary classifiers Evaluation of binary classifiers Evaluation of binary classifiers
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has abstract	Bei einer Klassifizierung werden Objekte anhand von bestimmten Merkmalen durch einen Klassifikator in verschiedene Klassen eingeordnet. Der Klassifikator macht dabei im Allgemeinen Fehler, ordnet also in manchen Fällen ein Objekt einer falschen Klasse zu. Aus der relativen Häufigkeit dieser Fehler lassen sich quantitative Maße zur Beurteilung eines Klassifikators ableiten. Häufig ist die Klassifikation binärer Natur, d. h., es gibt nur zwei mögliche Klassen. Die hier diskutierten Gütemaße beziehen sich ausschließlich auf diesen Fall. Solche binären Klassifikationen werden häufig in Form einer Ja/Nein-Frage formuliert: Leidet ein Patient an einer bestimmten Krankheit oder nicht? Ist ein Feuer ausgebrochen oder nicht? Nähert sich ein feindliches Flugzeug oder nicht? Bei Klassifikationen dieser Art gibt es zwei mögliche Arten von Fehlern: Ein Objekt wird der ersten Klasse zugeordnet, obwohl es der zweiten angehört, oder umgekehrt. Die hier beschriebenen Kennzahlen bieten dann eine Möglichkeit, die Zuverlässigkeit des zugehörigen Klassifikators (Diagnoseverfahren, Brandmelder, Fliegerradar) zu beurteilen. Ja-Nein-Klassifikationen weisen Ähnlichkeiten zu statistischen Tests auf, bei denen zwischen einer Nullhypothese und einer Alternativhypothese entschieden wird. (de) The evaluation of binary classifiers compares two methods of assigning a binary attribute, one of which is usually a standard method and the other is being investigated. There are many metrics that can be used to measure the performance of a classifier or predictor; different fields have different preferences for specific metrics due to different goals. For example, in medicine sensitivity and specificity are often used, while in computer science precision and recall are preferred. An important distinction is between metrics that are independent on the prevalence (how often each category occurs in the population), and metrics that depend on the prevalence – both types are useful, but they have very different properties. (en)
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foaf:isPrimaryTopicOf	wikipedia-en:Evaluation_of_binary_classifiers
is Link from a Wikipage to another Wikipage of	Diagnostic odds ratio Go/no go Accuracy and precision Receiver operating characteristic Sensitivity and specificity Shock detector F-score Outline of machine learning
is foaf:primaryTopic of	wikipedia-en:Evaluation_of_binary_classifiers

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