I enhance the interpretability of the Random Forest (RF) by examining whether incorporating Fixed Effects (FEs) improves its predictive accuracy. I find that the RF performs worst without FEs, while its accuracy increases when FEs are included in the training sample. This improvement stems not from a reduction in period dummies but from FEs mitigating data heterogeneity. These results provide empirical evidence that incorporating FEs reduces noise, enhancing the predictive performance of the RF.