The LOO cross validations presented above show that the machine learning combined with the similarity input provide a good alternative to predict thermal rate constants of combustion reactions. To improve the robustness of the models, the models are re-trained on the DGALL generated by the hierarchical random sampling. Three best models are selected and averaged for each algorithm to reduce random errors. The average deviations of the models on the DGALL are given in Figure . Note that the models using the activation energy as one of the descriptors are re-trained as well to make the comparison on an equal footing. Clearly, the three machine learning models using the activation energy as a descriptor all yield small deviations on the training and validation sets while produce relatively large deviations on the prediction set. In contrast, the FNN and XGB-FNN models with similarity as a descriptor generally provide comparable deviations across the three data sets. For the reactions between alkanes and CH3, the averaged deviations on the prediction set for the three models with the similarity as a descriptor are less than 60%, which is visibly smaller than those using the activation energy as a descriptor (about 90%). The FNN and XGB-FNN models with similarity as a descriptor also perform better than those with activation energy as a descriptor for the reaction between alkanes and H/O. However, the XGB model using the activation energy as a descriptor behaves better than that of similarity for the reactions between alkanes and O. In conclusion, the models developed in this work are "accurate" or close to "accurate".