K-fold cross-validation (CV) is a robust method for estimating generalization performance of supervised learning models. Although CV is more reliable than using a single hold-out test set, it is also
High dielectric constant organic semiconductors, often obtained by the use of ethylene glycol (EG) side chains, have gained attention in recent years in the efforts of improving the device performance
