Optimizing Age Classification Using Hyperparameter Tuning and Handling Imbalanced Dataset: An Algorithm Decision Forest training Algorithms Approach

Age classification is a specialist field, and imbalanced datasets and hyperparameter tuning are essential issues that can increase the superiority of classification models. This study proposes a new method for age classification optimization using the Decision Forest training technique, focusing on hyperparameter tuning and handling imbalanced datasets. With substantial improvements in handling minority classes, this research aims to improve the model’s classification accuracy. A robust model that shows resistance to overfitting was created by utilizing the capabilities of the Decision Forest algorithm model. The accuracy and loss are excellent; the highest is K-FOLD 5: Accuracy: 0.9512 and Loss: 0.2172. This innovative study presents significant breakthroughs regarding age categorization and a workable approach for researchers to perform appropriate hyperparameter adjustments when dealing with imbalanced data. This represents a tremendous advance in the classification field and an exciting path of research and application