# Optimal Feature Set Size in Random Forest Regression
## 2 Feature Set Size (_mtry_)
### Random Forest Algorithm in Regression
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Figure 8: Comparison between the proposed search algorithm and the typical grid search method in terms of the relative RMSE and the relative computing time. The \(x\)-axis represents 56 datasets. The \(y\)-axis on the left is log(relative RMSE), which the \(y\)-axis on the right is the relative computing time, which is defined as log(Computing time of the proposed method/Computing time of the grid search algorithm). * [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Random forest machine learning algorithm predicts virologic outcomes among HIV infected adults in Lausanne, Switzerland using electronically monitored combined antiretroviral treatment adherence. _AIDS Care_**2020**, _33_, 530-560. [CrossRef] [PubMed]
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