Comparing Decision Tree And Gradient Boosting Algorithms In Predicting Stock Market Trends
Sudheer Nandi, Dr. Saurabh Singh
This study presents a comparative analysis of Decision Tree and Gradient Boosting algorithms in
predicting stock market trends. Utilizing a comprehensive dataset of historical stock prices and technical
indicators, the performance of both models was evaluated across key metrics, including accuracy,
precision, recall, F1-score, and ROC-AUC. The findings reveal that the Gradient Boosting algorithm
significantly outperforms the Decision Tree in all aspects, achieving higher accuracy, better precision,
and greater overall model robustness. The results highlight the superior capability of Gradient Boosting
in capturing complex, non-linear patterns in financial data, making it a more reliable tool for stock
market prediction. This research underscores the importance of advanced machine learning techniques
in financial forecasting and provides valuable insights for practitioners in the field.
