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Quantile regression models a conditional quantile of a response, rather than its conditional mean. Fitting by the check loss uses an asymmetric absolute-error penalty, so different values of $\tau$ describe different parts of the response distribution. Nonlinear models may use any identifiable continuous regression function, not only a linear predictor.