Dimensionality reduction plays a critical role in efficiently managing large and complex datasets in machine learning (ML) applications. This study presents an innovative integration of principal component analysis (PCA) and extreme gradient boosting (XGB) to model the hydrothermal carbonization (HTC) process. PCA effectively reduced the feature space from 18 to 9 principal components with minimal impact on model accuracy (R² decreased slightly from 0.8900 to 0.8480), significantly simplifying the model complexity. To enhance interpretability, one- and two-dimensional partial dependence plots (PDP) were employed, revealing key features and their interactions influencing HTC outcomes. This combined approach not only improves predictive performance but also provides meaningful insights into the underlying process variables, addressing common challenges of ML opacity. While the model demonstrates strong predictive capability, further experimental validation and extension to diverse biomass types are recommended to confirm practical applicability and enhance versatility. The proposed methodology offers a robust, interpretable, and computationally efficient framework for optimizing HTC and can guide future research involving high-dimensional datasets.
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