Solutions Are Not Universal
Upon comparison, the experts noted that the RF model trained with augmented data yielded significantly better recall than their baseline model (0.80 versus 0.66) and similar precision (0.90 versus 0.86).
“This highlights the need to comprehensively and methodically try different approaches,” they wrote. “This includes using the same algorithm by recalibrating the same model as well as refitting the model with new data.”
The authors added that retraining an entirely new model is more labor intensive but was worthwhile in their case. However, depending on the situation, updating a baseline model with new data might also suffice—it really just depends on the unique model and situation, the authors suggested before concluding:
Although recalibration or refitting models with new data may be sufficient in some cases, training and validating new machine learning algorithms may be necessary in other cases to address model drift and ensure predictive model performance.”
The study abstract can be viewed here.
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