Hyperparameter Tuning in Random Forest and Neural Network Classification: An Application to Predict Health Expenditure Per Capita

Abstract

There is a lack of literature about the classification performance improvement effect of hyperparameter tuning to predict health expenditure per capita (HE). In this study, the effect of hyperparameter tuning on classification performances of random forest (RF) and neural network (NN) classification tasks is compared for grouping member of World Bank (WB) countries in terms of HE. Data gathered from 188 member countries of WB for the year 2019. GDP per capita, mortality, life expectancy at birth and population aged 65 years and over are used as predictors. Number of trees and neurons in hidden layer are changed from 5 to 100 for RF and NN by changing k-fold parameter from 2 to 20. The dependent HE variable is transformed into binary categories, and the categories are well balanced (%50–%50). Classification performances of learning techniques are good (AUC > 0.95). RF (AUC = 0.9609) is superior to NN (AUC = 0.9596) in terms of average AUC values generated by hyperparameter tuning.