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The Sensitivity Conundrum – Random Forest or Boosting
2020
International Journal of Emerging Trends in Engineering Research
In the classification context, tree-based models are simple and useful for interpretation. However when it comes to model accuracy the single-tree model does not match the power of other supervised learning approaches. By aggregating trees a model's accuracy can be improved. Ensemble methods like random forest and boosting combine predictions from multiple models into one that is far superior to the individual models. Depending on the business goal, the accuracy paradox may come into play. The
doi:10.30534/ijeter/2020/56872020
fatcat:frmxpxtksbcgvbs6qqdf4ngieq