They do not guarantee accurate property prediction of fluorophores used in biomedical researches. Sumita and coworkers used density-functional theory (DFT) with the B3LYP hybrid functional and the 3-21G* basis set to evaluate the photophysical properties of molecules. One critical limitation of QM approaches is that they need more considerable computation resources than ML-based approaches. Although there are a few studies to predict the electronic properties of small organic molecules with ML approaches, their prediction accuracy was inferior to that of QM approaches. Ghosh and coworkers utilized multiple deep-learning (DL) methods to train a machine that predicts the electronic properties of molecules from their structures as inputs. They used multi-layer perceptron (MLP), convolutional neural network (CNN), and deep tensor neural network (DTNN) to predict the density of states from the DFT results of the QM7 and QM9 data set. Nakata and coworkers utilized the support vector machine (SVM) and the ridge regression to predict the HOMO-LUMO gaps of molecules. The root mean squared error (RMSE) between QM values and prediction value of their models spans from 0.36 to 0.48 eV. Note that prediction errors of ML studies are similar to that of the time-dependent density functional theory (TD-DFT) benchmarks against experiments. To the best of our knowledge, previous ML approaches predicted excitation energies of a molecule, but not oscillator strengths. Montavon and coworkers predicted the maximum absorption intensity and corresponding excitation energy from time-dependent Hartree-Fock results using a deep neural network. The RMSE of excitation energy of maximum intensity predicted with the existing deeplearning model (1.71 eV) exceeds the range (1.45 eV) of visible light (1.65-3.10 eV). This amount of prediction error appears to be rather large for practical molecular design tasks. Here, we developed statistical models that predict the electronic transitions of a molecule using random forests (RF) algorithm 30 from SMILES input. RF is known to have low overfitting risk and high accuracy. Another advantage of RF over DL is that it facilitates the analysis of relative feature importance. In this study, we identified specific fragments that play essential roles in determining oscillator strengths of electronic transitions of molecules.