The Residual Sum of Squares (RSS) is the loss function used in Linear Regression to find the weights.
$$ \operatorname{RSS} = \sum_{i=1}^{n} (y_i - \hat{y}_i)^2, \qquad \hat{y}_i = \beta_0 + x_i^{\mathsf{T}}\beta $$It is also the core error metric used to build and optimize regression decision trees by selecting the best split that results in the lowest total RSS for the resulting child nodes:
$$RSS = \sum_{i \in R_1} (y_i - \hat{y}_{R_1})^2 + \sum_{i \in R_2} (y_i - \hat{y}_{R_2})^2$$