The residual is the distance between \(\tilde{y}\) (what the model predicted) and the actual value \(y\).
It is particularly important for the Linear Regression model, that minimizes residual sum of squares (RSS) to estimate weights.
The residual is the distance between \(\tilde{y}\) (what the model predicted) and the actual value \(y\).
It is particularly important for the Linear Regression model, that minimizes residual sum of squares (RSS) to estimate weights.