Deeper affection, more consumptions: Consumer Decision-Making Among People with Different Levels of Intimacy—Evidence from fNIRS

Based on a study on intimate relationships and consumer decision-making, this dataset includes Python code for prediction using machine learning (with leave-one-out cross-validation) and hyperparameter optimization code for the logistic regression and support vector machine (SVM) models, which are the focus during the model validation phase.The entire optimization process was conducted in a Python 3.7 environment, with logistic regression and support vector machine models implemented using the Scikit-learn library. Please ensure that your computer environment meets the above configuration requirements. Through this Bayesian optimization-based automated hyperparameter tuning method, we significantly enhanced the model's predictive performance while effectively avoiding the complexities and uncertainties of manual parameter tuning.If you are interested in the original dataset, please contact the two corresponding authors and specify the purpose and requirements for using the data when reaching out.