19.3 °C; Hydroquinones: 24.6 °C) (Figure ). Training set prediction errors are low, likely due to overly fine-tuning the splits between similar features when training the decision trees due to sparse data, causing poor generalizability to the test set. Due to the sensitivity to sparse input data, along with the extrapolation limitations discussed previously, the random forest model is not a good model structure for these datasets and this type of study.