Extra Trees. When growing a tree in the Random Forest algorithm, at each node, only a subset of molecular properties (features or attributes in ML jargon) is considered for the subsequent tree splitting into other nodes (see Fig. ). The trees can be made even more random by using train thresholds for each feature instead of searching for the best possible threshold, similarly to the process employed by a regular Decision Tree. These extremely random trees are called an extremely randomized ensemble, known for short as Extra-Trees. The algorithm "trades more bias for a lower-variance", and, in this way, the Extra-Trees is much faster to train compared with the Random Forest because the most consuming task of growing a tree is to find the best possible threshold for each feature at every node, which is not an issue here given the present small size of the training set compared with more usual applications. In the present work, 125 trees were generated employing the GridSearch tool and used for prediction. creating a pre-established rule for growing the tree. In this tree, the rule is the number of nitro groups bonded to the ring (# 𝑁𝑂 ' ) less than 2. of a molecule given by the algorithm will be the average of the values from each of the 125 trees.