The article explores the limitations of Random Forest Regression, particularly its inability to extrapolate beyond the training data, unlike linear regression models. While Random Forest Regression is robust and effective with large datasets and missing values, it struggles with predicting values outside the range of the training set, posing a problem for applications requiring extrapolation. This limitation is linked to the algorithm's averaging mechanism across decision trees, which can only predict within the observed range. Potential solutions include using linear models, deep learning models capable of extrapolation, or combining predictors through techniques like stacking. Regression-Enhanced Random Forests (RERFs) are also suggested as a modification to address this issue by integrating strengths from penalized parametric regression. The article concludes with guidance on when to use Random Forest Regression, recommending it for non-linear data trends where extrapolation is not crucial, and advising against its use in time series data where trend identification is essential.