Description: Huber’s loss is a loss function used in robust regression, designed to be less sensitive to outliers compared to traditional squared loss. This function combines the characteristics of squared loss and absolute loss, making it particularly useful in situations where data may contain outliers that could distort the regression model. Huber’s loss is defined by a threshold parameter that determines when to apply squared loss and when to apply absolute loss. For errors smaller than this threshold, squared loss is used, allowing for finer model fitting; while for larger errors, absolute loss is applied, which is more robust against outliers. This duality allows Huber’s loss to maintain efficiency in parameter estimation while protecting the model from disproportionate influences of extreme values. In summary, Huber’s loss is a valuable tool in data analysis, especially in contexts where data integrity may be compromised by the presence of outliers, offering a balance between sensitivity and robustness in modeling complex relationships.
