SOME MODEL SELECTION CRITERIA AND THEIR APPLICATIONS

ABSTRACT

Model selection is a critical aspect of machine learning and statistical analysis, as it determines the suitability and effectiveness of the chosen model for a given task or dataset. This study investigates three prominent model selection criteria: the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and CrossValidation (CV). The research aims to evaluate and compare the performance of these criteria in selecting appropriate models for predicting income levels based on demographic and health-related variables. The methodology involves implementing linear regression models with varying predictor combinations and assessing their complexity and predictive accuracy using AIC, BIC, and RMSE (Root Mean Squared Error) from cross-validation. The analysis explores the trade-offs between model fit, complexity, and generalizability, providing insights into the strengths and limitations of each selection criterion. The results indicate that Model 2, which includes age and gender as predictors, emerges as the most suitable choice, striking a balance between predictive accuracy and model parsimony. Further exploratory data analysis reveals significant effects of age and gender on income prediction, although the model's overall explanatory power remains relatively low, highlighting the need for incorporating additional relevant variables. The study contributes to a comprehensive understanding of model selection criteria, offering practical recommendations for their effective utilization in data analysis and machine learning tasks. The findings emphasize the importance of considering multiple criteria, prioritizing generalizability, aligning selection with project goals, and refining models based on insights from exploratory analyses.