The averaged contribution weight of each feature to a predicted ℎ .# value computed by the four algorithms are collected in Table for the test set (11 molecules) and Table for the training set (42 molecules). Although we report the averaged contribution of the values computed by each algorithm, it is important to note that overall, for the test set, the percentage contribution of each feature for a given algorithm is similar. The ordering, thus the importance, of the features is the same, except for a slight inversion in the AdaBoost ordering of the ∑ 𝑄 ' (𝐶) (37% weight) and ∑ 𝑄 * (𝑁𝑂 ' ) (38%) properties. For the training group, the electron delocalization of the ring atoms (∑ 𝑄 ' (𝐶)) and the 𝑁𝑂 ' total dipole (∑ 𝑄 * (𝑁𝑂 ' )) are still by far the most dominant properties, but in contrast with the test group, it is followed by the number of the nitro groups (#𝑁𝑂 ' ) as the third most important and their charges, ∑ 𝑄 # (𝑁𝑂 ' ), as the forth.