A Transparent Hybrid Machine Learning and Explainability Approach for Classifying Environmental Attitudes

Nowadays, the performance of machine learning (ML) models in data-driven decision-making processes largely depends on the quality of the data and the selection of informative features. In high-dimensional datasets, the presence of redundant or noisy attributes can hinder the learning process, lead to overfitting, and reduce model interpretability. Therefore, feature selection is considered a vital pre-processing step that not only reduces computational burden but also enhances model accuracy, generalizability, and transparency.In this context, the present study introduces an interpretable ML framework as a representative application for the classification of environmental attitudes. The proposed framework integrates dimensionality reduction via Principal Component Analysis (PCA), feature ranking through Random Forest (RF), and explainable artificial intelligence (XAI) techniques to improve both prediction performance and model transparency. Among the implemented models, the Multilayer Perceptron (MLP) supported by PCA and RF-based feature selection achieved the highest classification accuracy (98.7%), effectively capturing complex non-linear attitudinal patterns. To enhance interpretability, SHAP (SHapley Additive exPlanations) analysiswas employed, identifying the key survey items that contribute most to model decisions. Overall, the findings demonstrate that the integration of feature engineering and XAI provides a robust methodological foundation for environmental behaviour analysis, contributing to the development of data-driven and effective sustainability strategies.