The greater the number of trees is likely to yield better predictions but also increases the time it takes to train. Moreover, the model eventually reaches a prediction ceiling where adding more trees will not improve the model. We optimized the number of trees in the random forest model, ranging from 10 to 1500, and recorded the training time, the Scikit-Learn built-in score function value for random forest models, which is comparable to the coefficient of determination, R 2 , and the root mean square error (RMSE) for to the test set. As indicated in Figure , 50 trees are the optimal number for the random forest, as it gives the optimal training time, of about 25 seconds, while having the highest score and lowest RMSE.