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In statistics, the Huber loss is a loss function used in robust regression, that is less sensitive to outliers in data than the squared error loss. A variant for classification is also sometimes used. ==Definition== The Huber loss function describes the penalty incurred by an estimation procedure . Huber (1964) defines the loss function piecewise by : This function is quadratic for small values of , and linear for large values, with equal values and slopes of the different sections at the two points where . The variable often refers to the residuals, that is to the difference between the observed and predicted values , so the former can be expanded to〔 Compared to Hastie ''et al.'', the loss is scaled by a factor of ½, to be consistent with Huber's original definition given earlier.〕 : 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Huber loss」の詳細全文を読む スポンサード リンク
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