Random Forest
Random Forest is an ensemble learning method that combines multiple decision trees to make predictions. It creates a set of decision trees by randomly selecting subsets of features and instances. The predictions from individual trees are combined to make the final prediction. Random Forest is known for its robustness and ability to handle high-dimensional data.
| Precision: | 85.94% |
| Accuracy: | 92.37% |
| Recall: | 91.19% |
| F1 Score: | 88.49% |
| ROC AUC Score: | 92.06% |