Cross-Domain Applications of Machine Learning: A Comparative Case Study from Iris Classification to Infrastructure Assessment

Abstract

This study evaluates the predictive power of linear and logistic regression models on the Iris dataset, emphasizing feature importance and model performance, and comparing with construction assessment. Machine learning's role in predictive analysis is explored, with the Iris dataset serving as a benchmark for classification and regression tasks. The research addresses the need for robust methodologies to enhance model accuracy and interpretability. The objectives include comparing linear, logistic regression, and ANN approaches to highlight their strengths and limitations. Methodologically, data preprocessing, feature scaling, and Python-based implementations were employed to ensure reliable outcomes. Results indicate that ANN outperforms MLR (93.33%) in this research due to its adaptability to nonlinear relationships, achieving higher accuracy (97%). This aligns with findings in construction assessment studies, where ANNs' advanced methodology also demonstrated superior performance over MLR. Future research should integrate advanced machine learning models such as Neural Architecture Search (NAS), multimodal approaches, and ensemble techniques—including bagging (e.g., Random Forest), boosting (e.g., AdaBoost, XGBoost), and stacking. Related methods, such as voting classifiers, blending, and mixture of experts, can further enhance feature selection, interpretability, and predictive performance across domains.