Research Question and Sub-Questions
1.5.1 Primary Research Question:
Which machine learning model—deep learning, Gradient Boosting Machines (GBM), or Random Forests—provides the most accurate predictions of retirement savings outcomes?
1.5.2 Sub-Questions:
i. How do the predictive accuracies of deep learning, GBM, and Random Forests compare when applied to retirement savings outcomes?
ii. What are the most significant financial and behavioral factors influencing retirement savings adequacy, and how do these factors vary across different models?
iii. How do these machine learning models perform across different demographic groups, such as age, income level, and risk tolerance?
iv. What are the practical implications of using these models for financial advisors and policymakers, particularly in terms of enhancing the financial security of individuals?
v. How does model interpretability impact the usability of these predictive tools in real-world retirement planning scenarios?
Evaluating Predictive Models for Retirement Savings Outcomes: A Comparative Study
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