Data on the Construction Processes of Regression Models

This CSV dataset (numbered 1–8) demonstrates the construction processes of the regression models using machine learning methods, which are used to plot Fig. 2–7. The CSV file of 1.LSM_R^2 (plotting Fig. 2) shows the data of the relationship between estimated values and actual values when the least-squares method was used for a model construction. In the CSV file 2.PCR_R^2 (plotting Fig. 3), the number of the principal components was varied from 1 to 5 during the construction of a model using the principal component regression. The data in the CSV file 3.SVR_R^2 (plotting Fig. 4) is the result of the construction using the support vector regression. The hyperparameters were decided by the comprehensive combination from the listed candidates by exploring hyperparameters with maximum R2 values. When a deep neural network was applied to the construction of a regression model, NNeur., NH.L. and NL.T. were varied. The CSV file 4.DNN_HL (plotting Fig. 5a)) shows the changes in the relationship between estimated values and actual values at each NH.L.. Similarly, changes in the relationships between estimated values and actual values in the case NNeur. or NL.T. were varied in the CSV files 5.DNN_ Neur (plotting Fig. 5b)) and 6.DNN_LT (plotting Fig. 5c)). The data in the CSV file 7.DNN_R^2 (plotting Fig. 6) is the result using optimal NNeur., NH.L. and NL.T.. In the CSV file 8.R^2 (plotting Fig. 7), the validity of each machine learning method was compared by showing the optimal results for each method. Experimental conditions Supply volume of the raw material: 25–125 mL Addition rate of TiO2: 5.0–15.0 wt% Operation time: 1–15 min Rotation speed: 2,200–5,700 min-1 Temperature: 295–319 K Nomenclature NNeur.: the number of neurons NH.L.: the number of hidden layers NL.T.: the number of learning times