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: | ||
| − | * True positive (TP): The case | + | * 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. | ||
Revisión de 01:47 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 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.
