A comparative analysis of random forest and autoencoder for commodity predictive analytics
Baljinder Kaur, Brahmaleen Kaur Sidhu, Gurjit Singh Bhathal · 2025
In many different industries, the application of predictive analysis in decision-making processes has grown. The aim of this study is to present a thorough comparison of Random Forest (RF) and Autoencoder, two well-known predictive analysis methodologies. Decision tree-based Random Forest is an ensemble learning technique, whereas Autoencoder is an unsupervised learning kind of artificial neural network. This study intends to help practitioners and researchers choose the best strategy for their predictive analysis assignments by thoroughly examining their methodologies, strengths, shortcomings, and real-world applications.