| Popis: |
Ensemble methods are algorithms that combine various models together to give higher accuracy than individual models. The ensemble methods used here are majority voting, XGBoost, and random forest. Several decision trees are combined using voting classifier, Random forest tree, and XGBoost. These are considered as the best universal models which are used here to compare the accuracies with other models. The datasets are being split randomly 9, 18, and 27 times, respectively. The decision tree model is applied and later combined with voting classifier. The descriptions of the methods are followed by an extensive empirical study over 10 publicly available datasets. An ensemble model with five classifiers is also implemented that give us the accuracy of the model, and later all the accuracies are compared. Finally, a comparison has been done with XGBoost and Random forest classifiers, which shows the effectiveness of the used ensemble methods for classification. |
