In comparing model performance on varying radii, we aimed to identify the optimal balance between providing the model with too much information (with a radius too large) and not enough information (with a radius too small to adequately capture local environmental factors) without sacrificing our predictive accuracy . Our results indicated that a radius of 3 Å most effectively addressed this bias-variance tradeoff between the fluorine atom's environmental complexity and the model's predictive accuracy . For model performance using datasets of radii of 4 Å and 5 Å, it is possible that too much extraneous information in the input features (that have little true correlation with the NMR shifts) was introduced into the models, potentially leading to overfitting and poor generalization .