13988: Rar

: It is generally more memory-efficient than strategies that constantly add new points to the dataset. Weaknesses :

Residual-based Adaptive Refinement is a strategy used to improve the accuracy and efficiency of by intelligently selecting training data points. 13988 rar

: 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 : : It is generally more memory-efficient than strategies