Example Concept: To enhance a decision tree model's accuracy, you can apply techniques such as hyperparameter tuning (adjusting tree depth, minimum samples per leaf, etc.), employing ensemble methods like bagging or boosting (e.g., Random Forest, AdaBoost), and ensuring data quality through preprocessing steps like handling missing values and feature scaling. These approaches help in reducing overfitting, improving generalization, and leveraging multiple models to enhance performance.