To predict the oscillator strength and the excitation energy, random forests regression (RFR), an ensemble learning method was applied as schematically shown in Figure . It operates by constructing multiple decision trees during training and averaging the prediction values of all trees for final prediction. Each tree starts from the root node encompassing all data points, and nodes are divided into two new sub-nodes. Splits are decided to minimize mean square error (MSE). We performed extensive searches of hyperparameters (the number of trees, leaves, and the ratio of features). Extension of each tree ended when the number of used features for the decision reached one-third of the dimension of the input vector. Our final RF model consists of 1000 trees and 8714 leaf nodes. Four models were constructed in total. Two models were trained with Vector 1-ECFP4 4096 bits, MACCS keys, and molecular descriptors-to predict the excitation energy and oscillator strength of a molecule independently. The other two models are trained with ECFP6 16386 bits to identify larger fragments.