Delta de Huber
O Huber Delta, frequentemente referido simplesmente como perda de Huber, is a popular função de perda used in análise de regressão within aprendizado de máquina. It combines the best properties of two other funções de perda: the erro quadrático médio (MSE) and the erro absoluto médio (MAE). The primary purpose of the Huber loss is to provide robustness against outliers in data sets.
Em termos mais técnicos, a função de perda de Huber é definida como:
L(delta) = { 0.5 * (delta)^2, if |delta| <= delta_threshold
k * (|delta| – 0.5 * delta_threshold), otherwise }
Here, delta represents the difference between the predicted value and the actual value, while delta_threshold is a parameter that determines the point at which the loss function transitions from quadratic to linear. When the error (delta) is smaller than the threshold, the function behaves like MSE, which is sensitive to small errors. When the error exceeds the threshold, it behaves like MAE, which is linear and less sensitive to outliers.
The advantage of using Huber loss is that it effectively reduces the influence of outliers on the overall loss calculation, allowing models to achieve better performance on data sets that may contain noisy measurements. Consequently, it is widely used in various regression tasks, especially in scenarios where integridade dos dados não pode ser garantido.
In practice, selecting the appropriate delta_threshold is crucial, as it controls the sensitivity of the loss function to outliers. A smaller threshold makes the loss function more robust to outliers, while a larger threshold behaves more like MSE.