<![CDATA[This study aims to improve the performance of Iris plant classification by integrating ensemble learning techniques with Explainable Artificial Intelligence (XAI) to achieve high accuracy while enhancing model interpretability. Random Forest, XGBoost, and AdaBoost algorithms are combined within a Voting Ensemble framework and evaluated using the Iris Plants dataset, which comprises 150 data samples distributed equally across three Iris species (50 samples per class: Iris setosa, Iris versicolor, and Iris virginica). The dataset exhibits a perfectly balanced class distribution, ensuring that no class imbalance correction was required. The Voting Ensemble model was evaluated using a hold-out test set (80:20 split) and further validated through 5-Fold Stratified Cross-Validation, yielding a mean cross-validation accuracy of 95.83% (±2.64%) and a test set accuracy of 93.33%. To enhance model transparency, the SHAP (SHapley Additive Explanations) method is applied to explain the contribution of each feature to the prediction outcomes. The Voting Ensemble model achieved an ROC AUC score of 0.9900 (macro-average), with Precision, Recall, and F1-Score each reaching 0.9333 (macro-average). Feature importance analysis reveals that petal length and petal width are the primary factors in the Iris species classification process. The strong correlation (r = 0.9991) between feature importance scores in the Random Forest model and SHAP values confirms the consistency and reliability of the model’s interpretability. These findings demonstrate that integrating ensemble learning with XAI not only improves predictive performance but also strengthens transparency and trust in machine learning models, particularly for plant classification tasks.]]>
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