Diferencia entre revisiones de «Fairness and bias correction in Machine Learning»
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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: | 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: | ||
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* False positive (FP): A case predicted to be in the positive class when the actual outcome is in the negative one. | * False positive (FP): A case predicted to be in the positive class when the actual outcome is in the negative one. | ||
* False negative (FN): A case predicted to be in the negative class when the actual outcome is in the positive one. | * False negative (FN): A case predicted to be in the negative class when the actual outcome is in the positive one. | ||
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| + | ==References== | ||
| + | <references /> | ||
Revisión de 01:56 10 dic 2019
Version in Spanish: Equidad y corrección de sesgos en Aprendizaje Automático
Work in progress.
Metrics[1]
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 where both the predicted and the actual outcome are in the positive class.
- True negative (TN): The case where both the predicted and the actual outcome are in the negative class.
- False positive (FP): A case predicted to be in the positive class when the actual outcome is in the negative one.
- False negative (FN): A case predicted to be in the negative class when the actual outcome is in the positive one.
References
- ↑ Sahil Verma; Julia Rubin, Fairness Definitions Explained, (IEEE/ACM International Workshop on Software Fairness, 2018).
