Diferencia entre revisiones de «Fairness and bias correction in Machine Learning»
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(Página creada con «''Versión en español: Equidad y corrección de sesgos en Aprendizaje Automático'' Work in progress.») |
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| − | '' | + | ''Version in Spanish: [[Equidad y corrección de sesgos en Aprendizaje Automático|Equidad y corrección de sesgos en Aprendizaje Automático]]'' |
Work in progress. | Work in progress. | ||
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| + | ==Metrics== | ||
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| + | Most statistical measures of fairness rely on different metrics, so we will start by defining them. When working with a binary classifier, both the predicted and the actual classes can take two values: positive and negative. Now let us start explaining the different possible relations between predicted and actual outcome: | ||
| + | * True positive (TP): The case when both the predicted and the actual outcome are in the positive class. | ||
| + | * | ||
Revisión de 01:43 10 dic 2019
Version in Spanish: Equidad y corrección de sesgos en Aprendizaje Automático
Work in progress.
Metrics
Most statistical measures of fairness rely on different metrics, so we will start by defining them. When working with a binary classifier, both the predicted and the actual classes can take two values: positive and negative. Now let us start explaining the different possible relations between predicted and actual outcome:
- True positive (TP): The case when both the predicted and the actual outcome are in the positive class.
