Utilizing the Adaboost Algorithm for Effective Predictive Money Management

Abstract

Adaboosting is a robust algorithm for predictive money management, offering numerous benefits to individuals, businesses, and financial institutions. It enhances predictive accuracy, enabling more informed financial decisions. Adaboosting also aids in risk mitigation by identifying potential risks and opportunities, thereby safeguarding assets and investments. It also aids in optimizing investment strategies, resulting in higher returns and efficient resource allocation. It also helps detect fraud, distinguishing between legitimate and suspicious financial activities. Adaboosting also contributes to cost reduction by minimizing unnecessary losses. Overall, Adaboosting is a valuable tool for financial security and prosperity. The study evaluates the efficacy of a predictive model using various criteria and graphical representations. The model has an accuracy rate of 0.57, indicating it can predict outcomes in 57% of scenarios. However, the F1 score, which evaluates precision and recall, shows consistent reliability. The model also has some false-positive errors, suggesting a potential trade-off between precision and recall capabilities. The study uses visual tools like bar graphs and heat maps to analyze data based on specific categories or classes. The study was conducted at the University of California, Santa Barbara, and consistency and performance over time can be evaluated using log loss and accuracy charts.

DOI - https://doi.org/10.65525/SVUP.9788199778009.2026.113-126