# Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography
## 6 Conclusions  
This study focuses on the first-time application of the Random Forest algorithm in modeling offset printing color control systems. The objective is to determine the optimal solid ink density of test samples during the pre-press stage of printing production, highlighting the method's strong practical applicability. To solve the problem of large error and low efficiency of traditional empirical methods, a large amount of relevant data was collected by designing, printing, and measuring samples. An optimal solid ink density prediction model for printed materials, based on random forest, was developed. It was then compared with gradient boosting, artificial neural network, polynomial regression, and support vector machine algorithms using MAE, RMSE, MSE, and other indices, leading to the following conclusions:  
Through theoretical analysis and the selection of appropriate characteristic factors, this paper establishes the relationship between the colorimetric value and the density value of printed materials. Experimental validation confirms that the Random Forest prediction model offers higher accuracy and a better regression fit for predicting the optimal solid ink density of printed materials in the production process. After ten-fold cross-validation and hyperparameter optimization, the coefficient of determination of the Random Forest prediction model reaches 0.969, and the RMSE values are improved by 0.0082 and 0.0355, respectively, compared to other prediction models, further confirming the superiority of the Random Forest model. This indicates that the Random Forest model has higher applicability in predicting the density values of printed materials. In contrast, SVR and ANN models have large errors and are not suitable for multi-dimensional colorimetric data processing scenarios. The primary contribution of this study lies in proposing an accurate matching method for determining the optimal solid ink density in offset printing. This approach provides direct guidance for adjusting printing press parameters, reducing paper waste, and enhancing color consistency. Moreover, it facilitates the transition from experience-driven to data-driven decision-making in the printing industry. A limitation of this study is the relatively small sample size, which impacts the accuracy of the machine-learning model in regression analysis. Increasing both the sample size and the range of density values in the dataset could improve the model's prediction accuracy. Future research could explore the application of the matching model in hardware facilities to develop a density-matching system that integrates data collection, training, and prediction, thereby enhancing production efficiency. Additionally, incorporating more features to determine the optimal solid ink density and studying the impact of different substrates on ink color rendering can enhance model accuracy. Exploring hybrid modeling approaches that combine physical models with machine learning will further improve adaptability to complex and variable production conditions. Finally, machine-learning models have consistently demonstrated their high efficiency in the printing industry. The applicability of the Random Forest model further enhances production processes in this industry and offers a novel approach to improving the quality of printed materials and advancing process technology. Conceptualization, H.F. and J.L. ; methodology, H.F.; software, J.L. and Y.Q. ; formal analysis and investigation, H.F., J.L. and L.P.; resources, J.L. ; writing--original draft preparation, H.F., L.P. and Y.Q. ; writing--review and editing, H.F., L.P. and J.L. ; funding acquisition, L.P. All authors have read and agreed to the published version of the manuscript. National Key Technologies R & D Program of China: No. SQ2023 YFB3200093. Zhejiang Provincial High-Level Talent Special Support Program & Zhejiang 'Ten Thousand Talents Plan': No. 2023R5212. Not applicable.