Adaboost
Adaboost is an ensemble learning method that combines multiple weak classifiers to create a strong classifier. It iteratively adjusts the weights of the weak classifiers to focus on difficult instances, improving overall accuracy. Adaboost is particularly effective in handling complex classification problems.
| Precision: | 84.47% | 
| Accuracy: | 91.07% | 
| Recall: | 88.81% | 
| F1 Score: | 86.59% | 
| ROC AUC Score: | 90.48% |