: While adaptive sampling approaches often rank and select points based on residual errors, RAR specifically chooses the "top k" largest residual points without necessarily differentiating between them further.
: Other sophisticated adaptive strategies can become computationally expensive as the number of training points accumulates over time. RAR is often viewed as a more balanced fit because it can refine the model without letting the training set grow uncontrollably. Strengths : 13988 rar
: It significantly improves the speed at which a model converges to a solution. : While adaptive sampling approaches often rank and