Decision trees are commonly used for both classification and regression. Decision trees are interpretable and versatile, making them valuable for a wide range of applications in data analysis and prediction. They work by recursively partitioning the data into subsets based on feature values, ultimately leading to a tree-like structure where each leaf node represents a predicted outcome. However, they are prone to overfitting since every data point can be explained with sufficient tree depth. This can be mitigated by using ensemble methods and carefully selecting the model parameters such as tree depth. We evaluate a number of different ensemble treebased methods including: random forest (RF), gradient boosted trees (XGBoost), natural gradient boosting with uncertainty estimation (NGBoost), and histogram gradient boosting (HGB), the implementation of which is inspired by LightGBM. Tree-based methods are able to handle non-linear relationships within the feature space, perform automatic feature selection, and can be interpreted by analyzing the decision boundaries established by the trees. RF is an ensemble of decision trees trained on various bootstrapped subsets of the entire dataset, with features randomly selected for branches within the trees. A final prediction and uncertainty is attained by consensus of the ensembles. XGBoost is an optimized implementation of gradient boosting that sequentially combines many weak individual predictive models to form a strong final ensemble predictive model, with each subsequent model trained to correct the errors of the previous models. However, this comes with trade-offs in the form of lack of interpretability, trialand-error hyperparameter tuning, and higher computational cost. NGBoost is similar to XGBoost, also learning the errors of previous trees through boosting, but predicts the parameters of a distribution rather than the regression value directly. NGBoost is useful when uncertainty estimation is desired and offers better interpretability through the analysis of the predicted distributions. HGB regression is much more efficient by virtue of first binning the input features and building the trees off of bins rather than floating point values. As a result, the number of split candidates and the computational cost are significantly reduced. As an added benefit, one of the bins is reserved for missing values, which is very useful for incomplete datasets as will be discussed below. The tree-based methods discussed above are almost indistinguishable in terms of predictive performance, except for XGBoost which performs slightly worse. Most importantly, tree-based methods perform best with ECFP (between R 2 = 0.58±0.02 and 0.59±0.02), and Mordred descriptors (between R 2 = 0.56±0.03 and 0.6±0.03) because ECFP and Mordred descriptors extract relevant chemical and molecular features unlike other molecular representations such as SMILES or one-hot encodings.