Decision Tree using Feature Grouping

Machine learning (ML) is a crucial junction of statistics and computer science in the quickly developing field of artificial intelligence. In particular, when conventional algorithms fail, this research investigates the creation of algorithms that give priority to data-driven insights over rigid instructions. Among the many tools that ML provides, decision trees and random forests have drawn attention for their accuracy in making decisions. However, the introduction of the random forest technique, an ensemble approach that combines predictions from various trees for improved accuracy, was prompted by the limits of individual decision trees in handling large datasets and complicated relationships. A innovative data categorization method based on the idea of feature correlation is at the heart of our study. We found discrete clusters and created a decision tree specifically for each by analyzing the interactions between dataset elements, resulting in detailed and accurate classifications. A thorough analysis of numerous datasets showed that our technique consistently outperformed more traditional approaches. This work essentially emphasizes the transformational potential of ML in data classification, highlighting the ability of feature correlation to improve accuracy and efficiency in the constantly evolving field of artificial intelligence.