This research study compares various machine learning algorithms for predicting self-assessments of cognitive functioning, specifically memory, using data from the Health and Retirement Study involving individuals over 50 years old. The study reveals that while different algorithms yield only minor differences in predictive performance, the theoretical and social context of the data plays a significant role in model construction. Study Objective: The aim is to identify which machine learning algorithm best predicts memory assessments using a dataset from the Health and Retirement Study. Algorithms Compared: The study examines several algorithms including ordinal regression, linear regression, neural networks, and Bayesian approaches, evaluating their effectiveness in predicting memory outcomes. Predictors Used: Key predictors in the models include self-rated hearing, age, education, work status, gender, marital status, and life satisfaction, derived from a sample of 15,408 respondents. Results Summary: The results indicate moderate levels of variance explained by the models, with significant predictors identified across all algorithms, particularly highlighting the importance of hearing and education. Model Performance: The goodness of fit measures show that the models provide similar levels of predictive accuracy, indicating modest explainability. Conclusions: The study concludes that while predictive performance varies slightly among algorithms, understanding the underlying theoretical connections is crucial for developing explainable machine learning models in social sciences.